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Objective Recognization of hand movement patterns by using electromyography (EMG) signal is the key to controlling modern rehabilitation prosthetic hand. It can promote the common progress of biotechnology and mechatronics technology. To recognize more hand movement patterns by fewer electrodes is one of the difficulties. In order to make the best of EMG information, the article proposes a method of hierarchical classification. Methods Firstly, a method of recognizing the hand movement patterns based on hierarchical classification is proposed. The method utilizes the multiple characteristics of the classified objects. Integrate EMG is used for the feature value and the linear discriminant function actualizes presorting. Then, an autoregressive (AR) model is established and its parameters are used for the features. Artificial neural network can be used to classify finely. Finally, comparative experiments are carried out to verify. Results The experiments show that the eight common hand movements can be recognized effectively by two surface EMG electrodes. Conclusions The method indicates that the more movement patterns can be recognized by fewer electrodes and it performs better than the method without hierarchical classification.