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Failure form of gear box parts is varied, and typical faults have limited training samples. In order to improve the accuracy and efficiency of gearbox fault diagnosis, decision tree with multiple support vector machine (SVM) was proposed based on the early gearbox fault diagnosis methods. Classification based on decision tree on the advantage of fast speed, high efficiency and support vector machine (SVM) in binary classification has outstanding characteristics of small sample build multivariate classification model, in different typical fault case to extract the gearbox vibration signal characteristic parameters as the fault feature vector training model, and testing samples. The results show that this method not only can complete the model learning training in the case of small samples ,but also has been greatly improved over the neural network method in terms of the recognition performance,and can be effectively applied to gearbox fault diagnoisis. The results show that this method not only can complete the model learning training in the case of small samples,but also ha.