In this paper we present our investigations on the potential of wavelet-based preprocessing for surface electromyographic speech recognition. We implemented several variants of the Discrete Wavelet Transform and applied them to electromyographical data. First we examined different transforms with various filters and decomposition levels and found that the Redundant Discrete Wavelet Transform performs the best among all tested wavelet transforms. Furthermore, we compared the best wavelet transform to our EMG optimized spectral- and time-domain features. The results showed that the best wavelet transform slightly outperforms the optimized features with 30.9% word error rate compared to 32% for the optimized EMG spectral and time-domain features. Both numbers were achieved on a 108 word vocabulary test set using phone based acoustic models trained on continuously spoken speech captured by EMG.