The human-computer interaction modes between operators and industrial robots are rather mechanized currently. In order to solve the problem, a hand gesture control method by using Kinect sensor as a hand gesture acquisition equipment to control industrial robots was proposed. Firstly, the method of combining depth threshold algorithm and hand bones points was used to extract the hand gesture images accurately from the data obtained by a Kinect infrared camera. In the process of extraction, the operator did not need to wear any equipment, while it had no requirements to operator location and background environment. Then the method of combining deep autoencoder network and Softmax classifier was used for hand gesture image recognition. The hand gesture recognition included pretraining and fine tuning. The greedy layerwise approach was leveraged to train each layer of network in turn in pretraining, while all layers of the neural network were treated as a whole to fine tune the parameters of the entire network in fine tuning. The hand gesture recognition accuracy was up to 99.846%. Finally, the experiments were conducted on self-developed industrial robot simulation platform, the good results had been achieved in one hand and both hands gestures. The experimental results show that the proposed method by using hand gesture to control the industrial robot is feasible and available.