The analysis and discrimination of action potentials, or "spikes", is a central issue to systems neuroscience research. Here we introduce a free open source software for the analysis and discrimination of neural spikes based on principal component analysis and different clustering algorithms. The main objective is to supply a friendly user interface that links the experimental data to a basic set of routines for analysis, visualization and classification of spikes in a consistent framework. The tool has been tested on artificial data sets, on multi-electrode extracellular recordings from ganglion cell populations in isolated superfused mouse, rabbit and turtle retinas, and on electrophysiological recordings from mouse visual cortex. Our results show that NEV2lkit is very reliable and able to satisfy the experimental demands in terms of accuracy, efficiency and consistency across experiments. It performs fast unit sorting in single or multiple experiments and allows the extraction of spikes from over large time intervals in continuously recorded data streams. The tool is implemented in C++ and runs cross-platform on Linux, OS X and Windows systems. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code, binary distributions for different operating systems and documentation are all freely available at http://nev2lkit.sourceforge.net .