In this paper, we aimed to develop a method for the automatic recognition of individual finger-tapping motion. Biodegradable piezoelectric film sensors were attached to the skin of a forearm near the wrist (16 channels) to measure small movements of the tendons during five-finger tapping. In the proposed method, the segments in which motion occurred were detected by calculating the total activity for all channels. A neural network is trained to classify tapping motion using the extracted data based on the total activity, thereby allowing the accurate classification of flexion/extension of each finger. We collected experimental data from five healthy young adults to verify the motion recognition accuracy of the proposed method. The results revealed that the proposed method can recognize five-finger tapping motions with high accuracy (flexion/extension of each finger: 92.0%; time-series tapping motion: 88.4%).