Electromyogram (EMG) has been often used as a control signal of a prosthetic arm, which includes information on not only muscle force but operator's motor intention and mechanical impedance of joints. Most of previous researches, however, adopted the control methods of the prosthetic arms based on the EMG pattern discrimination and/or the force estimation from the EMG signals, and did not utilize any knowledge on tasks performed by amputees such as a grasping-an-object task and a spooning-soup task. In this paper, a new EMG pattern discrimination method is proposed using a statistically organized neural network and an event-driven task model. The neural network outputs aposterioriprobabilities of motions depending on the EMG signals. The task model is represented using a Petri net to describe the task dependent knowledge, which is used to modify the neural network's output. Experimental results show that the use of the task model significantly improves the accuracy of the EMG pattern discrimination.