Numerous methods have been developed to detect and sort single unit neuron activity from extracellular recordings. Because of the high variability in the neuron waveform shapes, equipment and recording settings, there is no single best performance algorithm. In recent years, several wavelet based detection algorithms have been proposed as methods for detecting spikes in extracellular recordings. The characteristics of the wavelet family and scale used play an essential role on the detection performance. On previous works the wavelet selection is done by visual inspection of the wavelet waveform. We have created a Matlab based software library that can characterize a wavelet’s ability to detect events on a signal, it will generate Receiver Operating Characteristic curves (ROC) and the Area Under the Curve (AUC) as comparison measurement information. This can be used to find the best detecting wavelet for certain data. Our software library also includes a spike sorting algorithm composed of a wavelet based spike detection step and a t-distribution expectation maximization clustering.