An MLP classifier outputs a posterior probability for each class. With noisy data, classification becomes less certain, and the entropy of the posteriors distribution tends to increase providing a measure of classification confidence. However, at high noise levels, entropy can give a misleading indication of classification certainty. Very noisy data vectors may be classified systematically into classes which happen to be most noise-like and the resulting confusion matrix shows a dense column for each noise-like class. In this article we show how this pattern of misclassification in the confusion matrix can be used to derive a linear correction to the MLP posteriors estimate. We test the ability of this correction to reduce the problem of misleading confidence estimates and to enhance the performance of entropy based full-combination multi-stream approach. Better word-error-rates are achieved for Numbers95 database at different levels of added noise. The correction performs significantly better at high SNRs.