diff --git a/egs/rm/s5/RESULTS b/egs/rm/s5/RESULTS
index b515804cfc2..1014fce03ed 100644
--- a/egs/rm/s5/RESULTS
+++ b/egs/rm/s5/RESULTS
@@ -229,6 +229,9 @@ for x in exp/nnet2_online_wsj/nnet_ms_a_smbr_0.00005/1/decode_*; do grep WER $x/
 %WER 7.33 [ 919 / 12533, 80 ins, 153 del, 686 sub ] exp/nnet2_online_wsj/nnet_ms_a_smbr_0.00005/1/decode_ug_epoch3/wer_13
 %WER 7.36 [ 923 / 12533, 85 ins, 148 del, 690 sub ] exp/nnet2_online_wsj/nnet_ms_a_smbr_0.00005/1/decode_ug_epoch4/wer_13
 
+### chain results ###
+# current best chain result with TDNN (check local/chain/run_tdnn_5f.sh)
+%WER 2.94 [ 369 / 12533, 51 ins, 71 del, 247 sub ] exp/chain/tdnn_5f/decode/wer_3_0.5
 
 ### nnet1 results ###
 
diff --git a/egs/rm/s5/local/chain/run_tdnn_5f.sh b/egs/rm/s5/local/chain/run_tdnn_5f.sh
new file mode 100644
index 00000000000..3cd46707ef3
--- /dev/null
+++ b/egs/rm/s5/local/chain/run_tdnn_5f.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+
+# this script is a modified version of swbd/run_tdnn_5f.sh
+
+set -e
+
+# configs for 'chain'
+stage=0
+train_stage=-10
+get_egs_stage=-10
+dir=exp/chain/tdnn_5f
+
+# training options
+num_epochs=12
+initial_effective_lrate=0.005
+final_effective_lrate=0.0005
+leftmost_questions_truncate=-1
+max_param_change=2.0
+final_layer_normalize_target=0.5
+num_jobs_initial=2
+num_jobs_final=4
+minibatch_size=128
+frames_per_eg=150
+remove_egs=false
+
+# End configuration section.
+echo "$0 $@"  # Print the command line for logging
+
+. cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+# The iVector-extraction and feature-dumping parts are the same as the standard
+# nnet2 setup, and you can skip them by setting "--stage 4" if you have already
+# run those things.
+
+ali_dir=exp/tri3b_ali
+treedir=exp/chain/tri4_2y_tree
+lang=data/lang_chain_2y
+
+local/online/run_nnet2_common.sh --stage $stage || exit 1;
+
+if [ $stage -le 4 ]; then
+  # Get the alignments as lattices (gives the CTC training more freedom).
+  # use the same num-jobs as the alignments
+  nj=$(cat exp/tri3b_ali/num_jobs) || exit 1;
+  steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd -l q=all.q" data/train \
+    data/lang exp/tri3b exp/tri3b_lats
+  rm exp/tri3b_lats/fsts.*.gz # save space
+fi
+
+if [ $stage -le 5 ]; then
+  # Create a version of the lang/ directory that has one state per phone in the
+  # topo file. [note, it really has two states.. the first one is only repeated
+  # once, the second one has zero or more repeats.]
+  rm -rf $lang
+  cp -r data/lang $lang
+  silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
+  nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
+  # Use our special topology... note that later on may have to tune this
+  # topology.
+  steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
+fi
+
+if [ $stage -le 6 ]; then
+  # Build a tree using our new topology.
+  steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
+    --leftmost-questions-truncate $leftmost_questions_truncate \
+    --cmd "$train_cmd -l q=all.q" 1200 data/train $lang $ali_dir $treedir
+fi
+
+if [ $stage -le 7 ]; then
+  steps/nnet3/chain/train_tdnn.sh --stage $train_stage \
+    --xent-regularize 0.1 \
+    --leaky-hmm-coefficient 0.1 \
+    --l2-regularize 0.00005 \
+    --jesus-opts "--jesus-forward-input-dim 200  --jesus-forward-output-dim 500 --jesus-hidden-dim 2000 --jesus-stddev-scale 0.2 --final-layer-learning-rate-factor 0.25" \
+    --splice-indexes "-1,0,1 -2,-1,0,1 -3,0,3 -6,-3,0" \
+    --apply-deriv-weights false \
+    --frames-per-iter 1000000 \
+    --lm-opts "--num-extra-lm-states=200" \
+    --get-egs-stage $get_egs_stage \
+    --minibatch-size $minibatch_size \
+    --egs-opts "--frames-overlap-per-eg 0" \
+    --frames-per-eg $frames_per_eg \
+    --num-epochs $num_epochs --num-jobs-initial $num_jobs_initial --num-jobs-final $num_jobs_final \
+    --feat-type raw \
+    --online-ivector-dir exp/nnet2_online/ivectors \
+    --cmvn-opts "--norm-means=false --norm-vars=false" \
+    --initial-effective-lrate $initial_effective_lrate --final-effective-lrate $final_effective_lrate \
+    --max-param-change $max_param_change \
+    --cmd "$decode_cmd" \
+    --remove-egs $remove_egs \
+    data/train $treedir exp/tri3b_lats $dir  || exit 1;
+fi
+
+if [ $stage -le 8 ]; then
+  steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 4 \
+    data/test exp/nnet2_online/extractor exp/nnet2_online/ivectors_test || exit 1;
+fi
+
+if [ $stage -le 9 ]; then
+  # Note: it might appear that this $lang directory is mismatched, and it is as
+  # far as the 'topo' is concerned, but this script doesn't read the 'topo' from
+  # the lang directory.
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang $dir $dir/graph
+  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+    --extra-left-context 20 --scoring-opts "--min-lmwt 1" \
+    --nj 20 --cmd "$decode_cmd" \
+    --online-ivector-dir exp/nnet2_online/ivectors_test \
+    $dir/graph data/test $dir/decode || exit 1;
+fi
+
+if [ $stage -le 10 ]; then
+  utils/mkgraph.sh --self-loop-scale 1.0 data/lang_ug $dir $dir/graph_ug
+  steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
+    --extra-left-context 20 \
+    --nj 20 --cmd "$decode_cmd" \
+    --online-ivector-dir exp/nnet2_online/ivectors_test \
+    $dir/graph_ug data/test $dir/decode_ug || exit 1;
+fi
+wait;
+exit 0;