The data description toolbox wants to provide tools, classifiers and evaluation functions for the research of one-class classification (or data description). In dd_tools it is possible to define special one-class datasets and one-class classifiers. Furthermore, the toolbox provides methods for generating artificial outliers, estimating the different errors the classifiers make (false positive and false negative errors), estimating the ROC curve, the AUC (Area under the ROC curve) error, the precision-recall curve and mean precision, and many classifiers. The toolbox is an extension of the PRTools toolbox, in which Matlab objects for prmapping and prdataset are defined. The data description toolbox uses these objects and their methods, but extends (and sometimes restricts) it to one-class classification. This means, that before you can use it to its full potential, you need to know a bit about PRTools. When you are completely new to pattern recognition, Matlab or PRTools, please familiarize yourself a bit with them first.