diff --git a/src/operator/tensor/broadcast_reduce_op.h b/src/operator/tensor/broadcast_reduce_op.h
index fc51d8af0f01..9fec6cd1255a 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -312,6 +312,23 @@ inline bool ReduceAxesShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+inline bool ReduceMinMaxAxesShape(const nnvm::NodeAttrs& attrs,
+                                  mxnet::ShapeVector *in_attrs,
+                                  mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known((*in_attrs)[0])) return false;
+  CHECK_GT((*in_attrs)[0].Size(), 0U)
+    << "Reduction input's size should > 0 "
+    << (*in_attrs)[0];
+  const ReduceAxesParam& param = nnvm::get<ReduceAxesParam>(attrs.parsed);
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     ReduceAxesShapeImpl((*in_attrs)[0], param.axis,
+                                         param.keepdims, param.exclude));
+  return true;
+}
+
+
 inline bool NormType(const nnvm::NodeAttrs& attrs,
                      std::vector<int> *in_attrs,
                      std::vector<int> *out_attrs) {
@@ -1488,6 +1505,16 @@ void PickOpBackward(const nnvm::NodeAttrs& attrs,
   .add_argument("data", "NDArray-or-Symbol", "The input")       \
   .add_arguments(ReduceAxesParam::__FIELDS__())
 
+#define MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(name)             \
+  NNVM_REGISTER_OP(name)                                        \
+  .set_num_inputs(1)                                            \
+  .set_num_outputs(1)                                           \
+  .set_attr_parser(AxesParamParser<ReduceAxesParam>)            \
+  .set_attr<mxnet::FInferShape>("FInferShape", ReduceMinMaxAxesShape)  \
+  .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>) \
+  .add_argument("data", "NDArray-or-Symbol", "The input")       \
+  .add_arguments(ReduceAxesParam::__FIELDS__())
+
 #define MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(name)               \
   NNVM_REGISTER_OP(name)                                            \
   .set_num_outputs(1)                                               \
diff --git a/src/operator/tensor/broadcast_reduce_op_value.cc b/src/operator/tensor/broadcast_reduce_op_value.cc
index f4231917e90d..f890963c2cf1 100644
--- a/src/operator/tensor/broadcast_reduce_op_value.cc
+++ b/src/operator/tensor/broadcast_reduce_op_value.cc
@@ -186,7 +186,7 @@ MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_nanprod)
 .set_num_inputs(3)
 .set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::nanprod_grad>);
 
-MXNET_OPERATOR_REGISTER_REDUCE(max)
+MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(max)
 .add_alias("max_axis")
 .describe(get_reduce_axes_description("max", __LINE__))
 .set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::maximum>)
@@ -200,7 +200,7 @@ MXNET_OPERATOR_REGISTER_REDUCE_BACKWARD(_backward_max)
 .set_num_inputs(3)
 .set_attr<FCompute>("FCompute<cpu>", ReduceAxesBackwardUseInOut<cpu, mshadow_op::eq>);
 
-MXNET_OPERATOR_REGISTER_REDUCE(min)
+MXNET_OPERATOR_REGISTER_MINMAX_REDUCE(min)
 .add_alias("min_axis")
 .describe(get_reduce_axes_description("min", __LINE__))
 .set_attr<FCompute>("FCompute<cpu>", ReduceAxesCompute<cpu, mshadow::red::minimum>)
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index f2d8a1b2524f..2a8e7d6e6698 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -6990,6 +6990,21 @@ def test_float16_min_max():
     assert np.finfo('float16').max == mx.nd.max(a).asscalar()
 
 
+@with_seed()
+@mx.use_np_compat
+def test_zero_size_min_max():
+    def min():
+        a = mx.nd.zeros(shape=(5, 0))
+        a.min()
+
+    def max():
+        a = mx.nd.zeros(shape=(5, 0))
+        a.max()
+
+    assert_raises(MXNetError, min)
+    assert_raises(MXNetError, max)
+
+
 @with_seed()
 def test_squeeze_op():
     def check_squeeze_op(shape, axis=None):