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A sequential test procedure for the classification of statistically dependent patterns is developed. The test is based on the optimum (Bayes) compound decision theory and the theory of Wald's sequential probability ratio test (SPRT). The compound sequential probability ratio (SPRT) is shown to be recursively computable at every instant of the decision process. A two-class recognition problem with first-order Markov dependence among the pattern classes is considered for the purpose of comparing the performance of the CSPRT with that of Wald's SPRT. It is shown that when the pattern classes are statistically dependent the CSPRT requires, on the average, fewer features per pattern than Wald's equally reliable SPRT. Finally, the results of computer simulated recognition experiments using CSPRT and other sequential and nonsequential decision schemes are discussed in detail.