Abstract: The application of the conventional back propagation neural networks (BPNNs) in the fault diagnosis of analog circuits might lead to a complex architecture for the NN and false diagnosis. A new analog fault diagnosis method,which is based on wavelet decomposition,genetic algorithm (GA) and neural networks (NNs),is proposed in this paper. The proposed method uses wavelet transform,principal component analysis (PCA) and normalization to deal with the node voltages of the circuit under test (CUT) to extract fault features,where the overlap of the features can be minimized. Under considering the shortcomings of that BPNNs easily fall into local minima,the proposed approach selects GA to optimize the structure and original weight distribution of BP networks. Finally,the experiments of the applications of our proposed method are expounded in this paper. The testing system based on the proposed method is built,and the application results further verified the effectiveness of our proposed method.