This paper details a strategy of discriminating finger motions using surface electromyography (EMG) signals, which could be applied to teleoperating a dexterous robot hand or controlling the advanced multi-fingered myoelectric prosthesis for hand amputees. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG signal classification system was established based on the surface EMG signals from the subject's forearm. Four pairs of electrodes were attached on the subjects to acquire the signals during six types of finger motions, i.e. thumb extension, thumb flexion, index finger extension, index finger flexion, middle finger extension, and middle finger flexion. In order to distinguish these finger motions. A combination of autoregressive (AR) model and an Artificial Neural Network (ANN) was used in the system. The discrimination procedure consists of two steps. Firstly, the AR model is used to preprocess the surface EMG signals to reduce the scale of the data. These data will be imported into the myoelectric pattern classifier. Secondly the coefficients of AR model are imported into the ANN to identify the finger motions. The experimental results show that the discrimination system works with satisfaction.