In this paper, we present a new technique for the recognition of hand gesture using decision tree method based on information entropy. Some rules are derived from the decision tree using training data, which can classify sixty-five different hand gestures. Normalization for all sensors in a DataGlove are also proposed to model the data variations of each sensor, which result from the same gesture variations. Compared with ANN, the proposed decision tree approach can not only improve the recognition performance by 12.2%, but also overcome the limitation of ANN in tedious training time.