This paper proposed an active finger recognition method using Bayesian filter in order to control a myoelectric hand. We have previously proposed a finger joint angle estimation method based on measured surface electromyography (EMG) signals and a linear model. However, when we estimate 2 or more finger angles by this estimation method, the estimation angle of the inactive finger is not accurate. This is caused by interference of surface EMG signal. To solve this interference problem, we proposed active finger recognition method from the amplitude spectrum of surface EMG signal using Bayesian filter. To confirm the effectiveness of this recognition method, we developed a myoelectric hand simulator that implements proposed recognition algorithm and carried out real-time recognition experiment.