When the surface electromyography (sEMG) signals change along with external or internal environment of the human body, general pattern classifiers will lead to a decrease of identification accuracy since they do not update their parameters adaptively. In order to adapt to the time-varying characteristics of sEMG signals, three kinds of adaptive algorithms for updating the parameters of a classifier during the use of artificial limb were introduced to improve the classification accuracy of time-variant sEMG signals. The pilot results of this study show that self-enhancing linear discriminant analysis is an effective solution and cycle substitution linear discriminant analysis presents the best performance but requires a large amount of calculations. The performance of the Kalman adaptive linear discriminant analysis is not prominent when it was used alone, and therefore it needs to be combined with other methods.