A computer model simulating the electrical activity of muscles of the upper arm during elbow motion is presented. The output of the model is an Electromyographic (EMG) signal. System identification is performed on the EMG signals using autoregressive moving average (ARMA) modelling. The calculated ARMA coefficients are then used as the feature set for pattern recognition. Pattern recognition is performed on the EMG signals to attempt to identify which of four possible motions is producing the signal. The results of pattern recognition are compared with results from pattern recognition of real EMG signals. The model is shown to be useful in predicting general trends found in the real data, but is not robust enough to predict accurate quantitative results. Simplifying assumptions about the filtering effects of body tissue, and about the size and position of muscles, are conjectured to be the most likely reasons the model is not quantitatively accurate. / Graduation date: 1992