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The roll bearing is a main driving device in the modern rotational machine equipments at the present time. In many cases, the accuracy of the instruments and devices used to monitor and control the system is highly dependent on the dynamic performance of the roll bearings. In this paper, A novel method of pattern recognition and fault diagnosis in roll bearing based on the wavelet-neural network is proposed according to the frequency spectrum characteristics of vibration signal. This paper presents an approach for roll bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis. Vibration simulation is used to assist in the design of various roll bearing fault diagnosis. The simulation testing results obtained indicate that neural networks can be effective agents in the diagnosis of various bearing faults through the measurement and interpretation of bearing vibration signal.