Wavelet neural networks (WNN) combing the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANNs) have attracted great interest and become a popular tool for various fields of mathematics and engineering. We describe here the application of WNN to the fault diagnosis of rotating machinery. In this paper, the wavelet network architecture for intelligent fault diagnosis is first introduced. Then an optimization method of neural network and a training algorithm is developed. Finally, Feedforward backpropagation neural network (BP) and wavelet neural networks are compared for fault diagnosis. The aim of this study is to examine the effective of the WNN model for fault diagnosis. Experiment results shows that the WNN has advantages of rapid training, generality and accuracy over feedforward backpropagation neural network.