In this paper, we introduce an evaluation of accelerometer-based gesture recognition algorithms in user dependent and independent cases. Gesture recognition has many algorithms and this evaluation includes Hidden Markov Models, Support Vector Machine, K-nearest neighbor, Artificial Neural Net-work and Dynamic Time Warping. Recognition results are based on acceleration data collected from 12 users. We evaluated the algorithms based on the recognition accuracy related to different number of gestures from two datasets. Evaluation results show that the best accuracy for 8 and 18 gestures is achieved with dynamic time warping and K-nearest neighbor algorithms.