Surface electromyography (sEMG) signals have been applied widely in prosthetic hand controlling. In the sEMG signal acquisition, wireless devices bring convenience, but also introduce signal missing due to interference or failure during data transmission. The missing signal may only last for tens of milliseconds, but have a great impact on the recognition. Researchers have employed various methods to complete missing sEMG data, but the completed signal may not totally fit the origins, and more extra calculation time will be spent. When recognizing hand gestures by sEMG from few sensors, to recognize the slightly or not serious signal missing, this study proposed a data split reorganization (DSR) strategy and a weight-based multiple neural network voting (WMV) method. To validate the proposed methods, controllable missing sEMG signals are generated artificially. Three time domain features are extracted based on non-overlapping sliding windows. The DSR is employed to make full use of the features, and then the WMV is utilized to recognize them. Nine subjects participated in the experiments, and the results indicate that the accuracy of the proposed methods is higher. For 5%, 10%, and 15% data missing ratios, the accuracy is 93.66%, 92.55%, and 91.19%, respectively. The Wilcoxon signed-rank test also demonstrates that these results are significantly superior to the situations in which the proposed methods are not applied. In the future, we will optimize the proposed methods to recognize the seriously missing sEMG signal.