Based on wavelet transform (WT), Fourier transform (FT) and neural network, a new fault diagnosis method of analog circuits was proposed. The proposed method uses wavelet transform (WT) and Fourier transform (FT) for fault feature extraction when the analog circuits are under different faulty situations. That is, we use WT to filter the disturbance influences (for example, noises) on the original signals to prevent the unrelated energies from being mixed with the effective signals. These signals are then analyzed by FT to obtain the frequency spectrum of the effective signals. And then, the energies of these signals are extracted and preprocessed by principal component analysis (PCA) and normalization as fault features. Meanwhile, considering that the probability neural networks (PNNs) have characteristics of simple structures, high-speed of training process and easy append training samples, we use this kind of neural networks for fault location. The diagnosis principles and procedures were outlined, And the satisfied diagnosis resolution and accuracies have been achieved by using the proposed method.