In connection with the development of artificial neural network (ANN) technologies, it is of interest to compare the robustness of classifier systems using the optimal voting rule and ANN. Robust algorithms are required to have high (though not the highest) efficiency in the case of planned situations, and acceptable efficiency in case of predetermined deviations from the plan (model). The robustness of the optimal voting rule and the neural network to the inaccuracy of computing the posteriori probabilities of partial solutions of the combined classifiers was investigated. It was found that in calculating the posterior probabilities with the error the neural network has provided a better performance than the optimal voting rule, showing the property robustness to the inaccuracy of input data. The use of classifiers utilizing ANN allows to increase robustness of algorithms in the signal receivers.