Detection of tool failure is very important in automated manufacturing. In this study, tool failure detection was conducted in two steps by using Wavelet Transformations and Restricted Coulomb Energy (RCE) type Neural Networks (WT-RCE). In the first step, data were compressed by using wavelet transformations, and unnecessary details were eliminated. In the second step, the estimated parameters of the wavelet transformations were classified by using Restricted Coulomb Energy (RCE)-type neural networks. The proposed approach was tested on experimental data (135 cases for training+42 separate cases for testing) and less than 3 percent error was observed. The proposed system can be easily trained to inspect data during transition and/or any complex cutting conditions.