In a robot-assisted surgery, a skillful surgeon can perform the operation excellently through flexible wrist motions and rich experience. However, there are little researches about the relationship between the wrist motion and electromyography (EMG) signal of surgeon. To this end, we introduce a classification framework of wrist motion to recognize the common wrist motion of the surgeon based on EMG signals. Generally, surface EMG (sEMG) signal has been widely used in prosthetic hand control and medical clinical application. Hence, in this paper, we utilize sEMG signals to evaluate the wrist motions. Eight channels of sEMG signals are captured through a MYO armband from the forearm of the subject. Different kinds of features based on EMG signal, root-mean-square, waveform length, and autoregressive are used to recognize wrist motion through linear discriminant analysis method. We test the impacts on recognition performance from the different sEMG features and different sampling moving windows length. Experimental results have verified the recognition performance of the presented approach. It is validated that the RMS feature can achieve best recognition performance with all different sampling moving windows length in comparison with the WL feature and AR feature.