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We are working on mapping multi-channel neural spike data, recorded from multiple cortical areas of an owl monkey, to corresponding 3D monkey arm positions. In earlier work on this mapping task, we observed that continuous function approximators (such as artificial neural networks) have difficulty in jointly estimating 3D arm positions for two distinct cases-namely, when the monkey's arm is stationary and when it is moving. Therefore, we propose a multiple-model approach that first classifies neural spike data into two classes, corresponding to two states of the monkey's arm: (1) stationary and (2) moving. Then, the output of this classifier is used as a gating mechanism for subsequent continuous models, with one model per class. In this paper, we first motivate and discuss our approach. Next, we present encouraging results for the classifier stage, based on hidden Markov models (HMMs), and also for the entire bimodal mapping system. Finally, we conclude with a discussion of the results and suggest future avenues of research.