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A signal analysis technique is developed for discriminating a set of lower arm and wrist functions using surface EMG signals. Data wete obtained from four electrodes placed around the proximal forearm. The functions analyzed included wrist flexion/extension, wrist abduction/adduction, and forearm pronation/supination. Multivariate autoregression models were derived for each function; discrimination was performed using a multiple-model hypothesis detection technique. This approach extends the work of Graupe and Cline  by including spatial correlations and by using a more generalized detection philosophy, based on analysis of the time history of all limb function probabilities. These probabilities are the sufficient statistics for the problem if the EMG data are stationary Gauss-Markov processes. Experimental results on-normal subjects are presented which demonstrate the advantages of using the spatial and time correlation of the signals. This technique should be useful in generating control signals for prosthetic devices.