The Minimum Entropy Method is studied with regard to its performance in removing multipath distortion frompassive transients, to improve the performance of classifiers. It was found that the method often works well if the associated multipath Green's functions kurtosis is high enough, and that signal stationarity is not required. We also found that, while there are usually a few filter lengths at which the best solutions are obtained with conventional convergence criteria, good solutions exist across a much broader range of filter lengths if the iterations are not allowed to proceed to convergence. That is, kurtosis needs to be increased, but not maximized. In many cases, two or three iterations is sufficient.