Knowledge Discovery in Databases Lubos Popelinsky We give a summary of the area of knowledge discovery in databases(KDD). After introductory part we specify the notion of knowledge in KDD and briefly explain reasons for KDD boom. We introduce the basic paradigm of inductive learning and discuss the role of visualization and statistics in KDD. After sumerizing the typical KDD tasks and after the list of the promising applications we introduce three systems, $C4.5$, $DBLearn$, and $CLEMENTINE$. $C4.5$ is a typical machine learning program for the efficient synthesis of decision trees. $DBLearn$ extends relational DBMS by learning facilities. $CLEMENTINE$ is an integrated tool which consists of data manipulation programs, visual programming, graphs as well as learning algorithms, neural networks and C4.5. After the conclusion we offer the list of electronic journals and archives relevant to KDD.