To improve the performance of gait recognition based on smartphone accelerometer, a recognition method based on Multiple Classifier Fusion (MCF) was proposed. Firstly, as the gait features extracted from the existing methods were relatively simple, the speed variation of the relative gradual acceleration extracted from each single gait cycle and the acceleration variation per unit time were taken as two new types of features (16 in total). Secondly, combing the new features with the frequently-used time domain and frequency domain features to form a new feature set, which could be used to train multiple classifiers with excellent recognition effect and short training time. Finally, Multiple Scale Voting (MSV) was used to fuse the output of the multiple classifiers to obtain the final classification result. To test the performance of the proposed method, the gait data of 32 volunteers were collected. Experimental results show that the recognition rate of new features for a single classifier is increased by 5.95% on average, and the final recognition rate of MSV fusion algorithm is 97.78%.