diff --git a/src/operator/nn/convolution.cc b/src/operator/nn/convolution.cc
index 79f2e800ee14..7aafe9d82f76 100644
--- a/src/operator/nn/convolution.cc
+++ b/src/operator/nn/convolution.cc
@@ -468,7 +468,7 @@ There are other options to tune the performance.
   else
     return std::vector<std::string>{"data", "weight", "bias"};
 })
-.set_attr<nnvm::FListInputNames>("FListOutputNames",
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
     [](const NodeAttrs& attrs) {
     return std::vector<std::string>{"output"};
 })
diff --git a/src/operator/nn/fully_connected.cc b/src/operator/nn/fully_connected.cc
index 475b63625166..278c130064fd 100644
--- a/src/operator/nn/fully_connected.cc
+++ b/src/operator/nn/fully_connected.cc
@@ -267,7 +267,7 @@ This could be used for model inference with `row_sparse` weights trained with `S
     return std::vector<std::string>{"data", "weight"};
   }
 })
-.set_attr<nnvm::FListInputNames>("FListOutputNames",
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
     [](const NodeAttrs& attrs) {
     return std::vector<std::string>{"output"};
 })
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 2486be04a52f..20cc4b511cc4 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -24,6 +24,7 @@
 import itertools
 from numpy.testing import assert_allclose, assert_array_equal
 from mxnet.test_utils import *
+from mxnet.base import py_str
 from common import setup_module, with_seed
 import unittest
 
@@ -5399,6 +5400,30 @@ def f(x, a, b, c):
         check_numeric_gradient(quad_sym, [data_np], atol=0.001)
 
 
+def test_op_output_names_monitor():
+    def check_name(op_sym, expected_names):
+        output_names = []
+
+        def get_output_names_callback(name, arr):
+            output_names.append(py_str(name))
+
+        op_exe = op_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
+        op_exe.set_monitor_callback(get_output_names_callback)
+        op_exe.forward()
+        for output_name, expected_name in zip(output_names, expected_names):
+            assert output_name == expected_name
+
+    data = mx.sym.Variable('data', shape=(10, 3, 10, 10))
+    conv_sym = mx.sym.Convolution(data, kernel=(2, 2), num_filter=1, name='conv')
+    check_name(conv_sym, ['conv_output'])
+
+    fc_sym = mx.sym.FullyConnected(data, num_hidden=10, name='fc')
+    check_name(fc_sym, ['fc_output'])
+
+    lrn_sym = mx.sym.LRN(data, nsize=1, name='lrn')
+    check_name(lrn_sym, ['lrn_output', 'lrn_tmp_norm'])
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()