This paper compares wavelet and STFT analysis for a speaker-independent stop classification task using the TIMIT database. In the designed experiment the HMM classifier had to assign each test token to one of the following stop classes [d,g,b,t,k,p,dx]. On 6332 stops the wavelet features obtained an overall accuracy of 86% which corresponds to a 14% relative error reduction compared to the STFT baseline system. Furthermore an analysis of the HMM misclassifications revealed that voiced stops were highly confused with their voiceless unaspirated counterparts.