Effects of running state and spindle speeds on the sound signals produced from a drill press are investigated. And the obtained sound signals by using of a sound level meter are analyzed in both time domain and frequency domain. It is evident that there is more high frequency energy for drilling sound signals with load than without load. And spindle speeds still affect their energy distribution of drilling sound signals. Using wavelet decomposition and wavelet packet decomposition, drilling sound signals are decomposed into a number of frequency bands. And energy percentages of the divided frequency bands are extracted to be the effective characteristics to recognize spindle speeds. Meanwhile, training error of different BP networks is compared to obtain the effective network for recognition spindle speeds. By using of the obtained network structure named 16-30-5, the study rate for training samples and the recognize rate for testing samples are all above 95%.