This paper introduced a pattern recognition method based on auto-regression (AR) model and bayes taxonomy. The proposed methodology consists of three steps. In the first step, the paper designs a circuit to collect surface electromyography (SEMG) signal. In the second step, Auto-regressive (AR) modeling in time series has been applied on people’s forearm muscle. So, EMG signal is preprocessed using AR-Model to extract features from MES. After calculated the coefficients of and AR model, we distill the AR coefficients as its eigenvector. In the third step, a bayes statistics algorithm is designed to classify the muscle movement of forearm. This paper finds this method has many advantages such as reducing error recognition rate and has a relative good result. It proves that there are some relations between motion pattern and AR coefficients. At the same time, this paper adopts virtual instrument technology to raise accuracy of measurement, reduce the cost and workload.