Neural Networks with Ellipsoidal Activation Functions close in upon a decision-making region by Gauss distribution for various patterns and are adapted for fault diagnosis well. It has the advantages, such as high precision in clssifying, few training time and well rejection for unknown pattern. For improving the antijamming performance, a multi-weight is adopted between input neuron and ellipsoidal neuron in the paper. The selection method of weights is also derived.By combining several neural networks with ellipsoidal activation functions, the multi-fault diagnosis can be diagnosed. the scale of network and training time are reduced observably and the ability for diagnosis is enhanced by the combination of networks. The simulation and experiments sustain the suggested method.