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A novel electromyographic (EMG) motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and nonlinearity analysis method is presented in this paper. This motion pattern classifier can successfully identify the flexion and extension of the thumb, the index linger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor poll icis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its high recognition capability.