diff --git a/TrainingExtensions/onnx/src/python/aimet_onnx/quantsim.py b/TrainingExtensions/onnx/src/python/aimet_onnx/quantsim.py
index dae142b0118..7d2880cf09a 100644
--- a/TrainingExtensions/onnx/src/python/aimet_onnx/quantsim.py
+++ b/TrainingExtensions/onnx/src/python/aimet_onnx/quantsim.py
@@ -895,9 +895,7 @@ def _set_src_qtzr(x: Product, consumer: Op, src_qtzr):
             _, out_qtzr, __ = self.get_op_quantizers(op)
 
             if not out_qtzr:
-                msg = 'Encoding propagation is only supported for ops with exactly ' \
-                      '1 output quantizer, but found output_quantizers[0] == []'
-                raise RuntimeError(msg)
+                continue
 
             if len(out_qtzr) != 1:
                 msg = 'Encoding propagation is only supported for ops with exactly ' \
diff --git a/TrainingExtensions/onnx/test/python/models/models_for_tests.py b/TrainingExtensions/onnx/test/python/models/models_for_tests.py
index 17f05baa7dc..3f0209039c0 100644
--- a/TrainingExtensions/onnx/test/python/models/models_for_tests.py
+++ b/TrainingExtensions/onnx/test/python/models/models_for_tests.py
@@ -2543,3 +2543,37 @@ def batchnorm_model_constants():
     onnx.checker.check_model(model, True)
     return model
 
+def integer_concat_model():
+    model = helper.make_model(
+        graph=helper.make_graph(
+            name='IntConcatModel',
+            inputs=[helper.make_tensor_value_info('model_input', TensorProto.FLOAT, shape=[10, 10])],
+            outputs=[helper.make_tensor_value_info('model_output', TensorProto.FLOAT, shape=[10, 10, 1])],
+            initializer=[],
+            nodes=[
+                helper.make_node('Constant', inputs=[], outputs=["constant_one"],
+                                 value=numpy_helper.from_array(np.array([1]).astype('int64')), name='one'),
+                helper.make_node(
+                    'Shape',
+                    inputs=['model_input'],
+                    outputs=['input_shape'],
+                    name='shape'
+                ),
+                helper.make_node(
+                    'Concat',
+                    inputs=['input_shape', 'constant_one'],
+                    outputs=['output_shape'],
+                    name='out_shape',
+                    axis=0
+                ),
+                helper.make_node(
+                    "Reshape",
+                    inputs=["model_input", "output_shape"],
+                    outputs=["model_output"]
+                )
+            ]
+        )
+    )
+    onnx.checker.check_model(model, True)
+    return model
+
diff --git a/TrainingExtensions/onnx/test/python/test_quantsim.py b/TrainingExtensions/onnx/test/python/test_quantsim.py
index f5801d4abbe..3f67dc39593 100644
--- a/TrainingExtensions/onnx/test/python/test_quantsim.py
+++ b/TrainingExtensions/onnx/test/python/test_quantsim.py
@@ -1579,6 +1579,15 @@ def forward(self, x):
                     _, out_qtzr, __ = sim.get_op_quantizers(cg_op)
                     assert _compare_encodings(out_qtzr[0].encodings[0], sim.qc_quantize_op_dict['output'].encodings[0])
 
+    def test_integer_concat(self):
+        """
+        When: Model contains unquantizable layers with op_type in quantsim.op_types_to_tie_qtzrs
+        Then: Error should not be thrown during quantsim init
+        """
+        model = models_for_tests.integer_concat_model()
+        with _apply_constraints(True):
+            sim = QuantizationSimModel(model)
+
     def test_clamp_activation_encodings(self):
         model = models_for_tests.matmul_add_model()
         dummy_input = {'model_input': np.expand_dims(np.identity(8, np.float32), axis=(0, 1))}