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In order to effectively use of the massive remote sensing data transmitted from satellites, in the process of testing for satellite real-time fault diagnosis, a method of satellite fault diagnosis based on BP neural network is proposed. The method includes offline autonomous learning and the real-time online fault diagnosis. The offline autonomous learning part automatically learns based on historical database and the updated samples, learning for neural network model is stored in the knowledge base. The real-time on-line fault diagnosis part is for the diagnosis of remote sensing data in real time online, based on the corresponding neural network model. To verify the method of satellite fault diagnosis based on BP neural network is effective and superior, with the control subsystem of the three axis stabilized near earth satellite as experimental object, the method is used to analysis the typical remote sensing data of infrared earth sensors and momentum wheel. By the experimental results analysis of this method and the method based on Kalman filtering, the experimental results show that the method is effective to satellite fault diagnosis.