Functional Electrical Stimulation (FES) is an effective and developing method to restore functions for paraplegic patients. In this research, we focused on the switching problem of FES, which is one of the obstacles that prevent FES from further practical uses. An adaptive switching for FES control for the lower limbs' activities of hemiplegic patients was developed, based on the consideration that, lower limbs' activities need the synchronization of limbs of both sides. Electromyogram (EMG) signals detected from normal side of hemiplegic patients were used to recognize the activities that the patients intend to do. The recognition results were utilized as the switching signals. However, motion patterns represented and analyzed by EMG are distinctive of individual variations and characteristic alternation, which inevitably result in classification errors in EMG analyzing. To overcome these problems, a feed-forward artificial neural network (ANN) was embedded in an on-line process to form an analyzing system that can adapt to individual characteristics and trace the nonstationary factor. Furthermore, in order to enable the analyzing system to recognize right timings from the EMG-described dynamical processes of activities, such as standing-up and walking, a practical training-set construction method that utilizes additional reference data was proposed. The proposed switching system was applied to a FES system that supports standing-up and walking for a hemiplegics subject, to verify the effectiveness.