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Surface electromyography (sEMG) signals are widely used in the recognition of hand gestures. Nowadays, researchers usually increase the number of sEMG signal measurement positions and extract multiple features to improve the recognition accuracy. In this paper, we propose a sEMG measurement position and feature optimization strategy for gesture recognition based on Analysis of Variance (ANOVA) and neural networks. Firstly, four channels of raw sEMG signals are acquired, and four time-domain features are extracted. Then different neural networks are trained and tested by using different data sets which are obtained based on the combination of different measurement positions and features. Finally, ANOVA and Tukey HSD testing are conducted based on the gesture recognition results of different neural networks. We obtain the optimal measurement position sets for gesture recognition when different feature sets are used, and similarly, the optimal feature sets when different measurement position sets are used. Our experimental results show that the feature set of zero crossing and integrated sEMG provides the highest gesture recognition accuracy, which is 94.83%, when four channels of sEMG signals are used; the optimal measurement position set when four sEMG signal features are used for hand gesture recognition is P1+P3+P4, which provides an accuracy of 94.6%.