A fault diagnosis method of Ball screw based on KPCA and Genetic GA-BP neural networks was proposed. First, synchronous acquisition of vibration signal of the Ball screw using 6 sensors in 2 points was done, and the original sample space was obtained by feature extraction. Then the dimension of the original sample space was reduced with the KPCA to eliminate the redundant information of the sample space. By introduced Genetic Algorithm, the randomness at selecting of traditional BP neural network initial weights and threshold was resolved, and three network in different types were established to diagnosis four different state of Ball screw including normal state, screw bending, broken ball and raceway wear. Results show that, ball screw fault diagnosis method based on KPCA and GA-BP neural network has significantly shorten the training time of the network, and effectively improve the recognition rate of the fault condition. 