This thesis develops a messenger system based on hand gesture recognition technique. Flex sensors and an accelerometer combined with a wearable gesture sensing device are used to measure the bending angle of each finger and the wrist. The measurements are transmitted to PC by RS232 transmission protocol. After filtering to smooth the data, these data are processed through the gesture recognition algorithm to generate the corresponding text. Finally, the users can interact with the others by the homemade messenger system interface through TCP / IP protocol. The proposed gesture recognition algorithm consists of two major steps, which first utilizes the concept of decision tree to classify the filtered measured data of different gestures, and then performs more delicate gesture recognition by a probability neural network to reach a verification result. Experiment shows that an improved verification rate with reduced computational burden of the problem is obtained by the proposed method.