To enhance the accuracy of gesture recognition using electromyogram(EMG) signals, we present an EMG signal feature extraction method based on timefrequence domain analysis. Firstly, a wireless EMG signal acquisition device is designed. Secondly, a gesture recognition method based on multivariate empirical mode decomposition (MEMD) and TeagerKaiser (TK) energy operator is proposed. Multidimensional scaling (MDS) method is used to reduce the dimensionality of multichannel features. then, linear discriminative classifier (LDA) is used to classify and recognize gesture features. The accuracy of this algorithm for UCI database can reach 9896%. The recognition accuracy for selfcollected database can reach 9937%. Meanwhile, F1 score also enhances significantly. The experiments verify that the method we proposed can reach a higher accuracy recognition results than other typical methods.