diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 3dd6123a5914..a740b1a52d6c 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -2185,6 +2185,42 @@ def convert_spacetodepth(node, **kwargs):
     )
     return [node]
 
+@mx_op.register("square")
+def convert_square(node, **kwargs):
+    """Map MXNet's square operator attributes to onnx's Pow operator
+    and return the created node.
+    """
+    onnx = import_onnx_modules()
+    name = node["name"]
+    proc_nodes = kwargs["proc_nodes"]
+    inputs = node["inputs"]
+
+    input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
+    input_node_a = proc_nodes[input_node_a_id].name
+
+    initializer = kwargs["initializer"]
+    data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
+
+    power2_name = "square_tensor" + str(kwargs["idx"])
+    tensor_node = onnx.helper.make_tensor_value_info(power2_name, data_type, (1,))
+    initializer.append(
+        onnx.helper.make_tensor(
+            name=power2_name,
+            data_type=data_type,
+            dims=(1,),
+            vals=[2],
+            raw=False,
+        )
+    )
+
+    node = onnx.helper.make_node(
+        "Pow",
+        [input_node_a, power2_name],
+        [name],
+        name=name
+    )
+    return [tensor_node, node]
+
 @mx_op.register("sum")
 def convert_sum(node, **kwargs):
     """Map MXNet's sum operator attributes to onnx's ReduceSum operator
diff --git a/tests/python-pytest/onnx/export/mxnet_export_test.py b/tests/python-pytest/onnx/export/mxnet_export_test.py
index 7cbc98003827..9f91369d667e 100644
--- a/tests/python-pytest/onnx/export/mxnet_export_test.py
+++ b/tests/python-pytest/onnx/export/mxnet_export_test.py
@@ -214,6 +214,30 @@ def test_spacetodepth():
 
     npt.assert_almost_equal(output[0], numpy_op)
 
+@with_seed()
+def test_square():
+    input1 = np.random.randint(1, 10, (2, 3)).astype("float32")
+
+    ipsym = mx.sym.Variable("input1")
+    square = mx.sym.square(data=ipsym)
+    model = mx.mod.Module(symbol=square, data_names=['input1'], label_names=None)
+    model.bind(for_training=False, data_shapes=[('input1', np.shape(input1))], label_shapes=None)
+    model.init_params()
+
+    args, auxs = model.get_params()
+    params = {}
+    params.update(args)
+    params.update(auxs)
+
+    converted_model = onnx_mxnet.export_model(square, params, [np.shape(input1)], np.float32, "square.onnx")
+
+    sym, arg_params, aux_params = onnx_mxnet.import_model(converted_model)
+    result = forward_pass(sym, arg_params, aux_params, ['input1'], input1)
+
+    numpy_op = np.square(input1)
+
+    npt.assert_almost_equal(result, numpy_op)
+
 if __name__ == '__main__':
     test_models("bvlc_googlenet", (1, 3, 224, 224), (1, 1000))
     test_models("bvlc_reference_caffenet", (1, 3, 224, 224), (1, 1000))
@@ -224,4 +248,4 @@ def test_spacetodepth():
     test_model_accuracy("inception_v1", (1, 3, 224, 224))
     test_model_accuracy("inception_v2", (1, 3, 224, 224))
 
-    unittest.main()
\ No newline at end of file
+    unittest.main()