Automatic speech recognition experiments are described in which several popular preprocessing and classification strategies are compared. Preprocessing is done either by linear predictive analysis or by bandpass filtering. The two approaches are shown to produce similar recognition scores. The classifier uses either linear time stretching or dynamic programming to achieve time alignment. It is shown that dynamic programming is of major importance for recognition of polysyllabic words. The speech is compressed into a quasi-phoneme character string or preserved uncompressed. Best results are obtained with uncompressed data, using nonlinear time registration for multisyllabic words.