This paper presents an automatic recognition method of the shaftorbit based on the Zernike moments and neural network.By re-extracting and reprocessing the original Zernike moments, the more sensitive momentf eatures for the shaft orbit identification were obtained, which simplified the designing of the succeeding neural network classifier. The simulation result shows that the accuracy of the Zernike moment based method is superior to the Hu′s geometric moment invariants based one. The proposed method was applied to automatically recognize the shaft orbit of a turbo-generator and a high-speed centrifugal air compressor, and the recognition results were incorporated with the spectrume nergy distribution features to diagnose the faults. It is shown that the accuracy of fault diagnosis is improved effectively by considering the orbit features,and the method is feasible.