By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnosing ability. By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnostics ability.