In order to solve the problem of large interference of environmental noise during speech recognition, an isolated word recognition algorithm based on empirical mode decomposition (EMD) and dynamic time warping (DTW) is proposed. In this method, the EMD algorithm is used to decompose the speech signal with poor performance into several basic mode functions (IMF) and remove the interference and noise in the original signal. Then, based on the DTW algorithm, the short-time zero crossing rate and short-time energy are used to detect the endpoint detection of speech signal. After the speech feature parameters are extracted, the speech signal is matched with the reference template. Finally, the shortest path between the reference template and the template to be measured is used as the recognition result. The simulation results show that the proposed algorithm can improve the recognition efficiency of speech and the accuracy of recognition.