Motorized spindle is an important functional part of CNC machine tool, and its advantages and disadvantages directly affect the quality of parts. A support vector machine regression model ( SVR) optimized by chaos genetic algorithm (CGA) is used for spindle fault diagnosis. The principle of the method is to use principal component analysis ( PCA) to reduce the dimensionality of the timefrequency characteristic vector of the vibration signal of electric spindle wear fault, and input the dimensionality reduced characteristic vector into the SVR model optimized by CGA parameters for training and testing. The results show that the accuracy of training and testing is 99. 272% and 95. 249% respectively, which can diagnose the wear fault of motorized spindle accurately.