Pianoplaying glove is one kind of emerging intelligent wearable equipment. By using the multiinertial sensors in glove, the gesture of piano player can be realtime perceived and analyzed. The learners can know in realtime whether the playing gesture is right.Thus,the efficiency and interest of piano learning can be improved and the cost of learning can be reduced effectively. Different from gestures in other application fields, piano playing gestures have the characteristics of diversity, rapidity, large dynamics and strong timevarying. In this study, the piano playing gesture recognition system based on inertial data glove and infrared detecting rod is designed. A method of gesture recognition for piano playing based on machine learning is proposed. The output of inertia data gloves and infrared detection rods are used as data sample. According to the characteristics of piano playing gestures, multimodal gesture features are extracted.Hierarchical recognition algorithm is adopted to improve the recognition effectiveness. Experimental results show that the proposed recognition method can better meet the needs of gesture recognition in piano playing. The recognition accuracy rate is better than 99%.