This paper summarizes results of the research project “Application of modern methods to data processing in the field of marketing research” which was solved at the Department of Informatics, Faculty of Business and Economics of Mendel University in Brno. The most of these results were presented at international conferences.It describes the use of knowledge discovery techniques on data from marketing research of consumers’ behaviour. The paper deals with issues of classification, cluster analysis, correlation and association rules.For classification there were used various algorithms: multi-layer perceptron neural network, self-organizing (Kohonen’s) maps, bayesian networks and generation of a decision tree. Beside Kohonen’s maps, which were tested in MATLAB software, all classification methods were tested in Weka software.In order to find clusters of the methods K-means, Expectation-Maximization, DBSCAN Weka was also used as software for clustering. Correlation analysis was done based on statistical approach. Generation of association rules was achieved by use of Apriori and the FP-growth algorithm in Weka.The paper describes above mentioned methods and shows achieved results of exploring data from marketing research on consumers’ behaviour.This article discusses the suitability of these methods usage on such data sets. It also suggests further research possibilities of knowledge discovery on consumers’ behaviour.