Character and gesture recognition are one of the most studied topics in recent years. Character recognition studies are generally based on image processing. Only a few studies can be found about character recognition as gesture recognition. Gesture recognition is making the computers understand human body movements by using different kind of knowledge of the environment. This knowledge can be obtained by image or sensor-based efforts. Accelerometer is the most used sensor in gesture recognition, so in this study a 3-axis accelerometer is used. In this thesis, English alphabet‟s lowercase characters are used. A ring-like device which contains accelerometer in it is used. After obtaining the acceleration data of each character with 20 repetitions, we apply filtering, segmentation and normalization preprocessing steps for each signal. Since there are different accelerations and decelerations between each repetitions, Dynamic Time Warping (DTW) algorithm has been chosen to determine the similarities between signals. DTW is an algorithm that uses the amplitude values of the signals, so it is weak to amplitudes that shift in time domain. To overcome this shortcoming, the method called Derivative Dynamic Time Warping (DDTW) has been applied to the acceleration signals. DTW and DDTW methods have been compared and we have found that even we remove the normalization step; DDTW gives better results than DTW. By comparison of linear alignment and DTW, the results show that DTW gives better recognition rates for signals with different accelerations and decelerations. DTW also gives better result for the different length signals.