There exist many data classification methods and algorithms; however the importance of them has not diminished. The data and information quantities increase as well as the diversity of information, so the question is how to reliably process the data. Various considerations emerge concerning what method to choose or which of them fits for data best, i.e. which of them would classify data most accurately and reliably. This work presents a classification method based on the statistics of Dirichlet mixtures. Dirichlet mixture combines more than one Dirichlet densities that are described by the same set of parameters but with different values of them. Such a Dirichlet mixture becomes sensitive to recognize differently distributed variables (data) and hence, utilization of the Dirichlet mixtures in classification can provide a powerful tool for classification of data of any kind. This thesis proposes a method describing how Dirichlet mixtures can be utilized for classification of data of any kind. With regard to this, a Dirichlet mixtures classifier is designed to classify data of any type and with any range of values. The designed classifier classifies numerical data as well as symbolic ones. The Dirichlet mixtures classifier is implemented in two ways: The first one concerns the classifier as the end-product for a user and the second one relates to a compiled library of classification routines. Using the Dirichlet mixtures classifier as the product, the user can classify data and... [to full text]