Data Mining, or Knowledge Discovery in Databases, is a fairly young research area that has emerged as a reply to the flood of data we are faced with nowadays. It tries to meet the challenge to develop methods that can help human beings to discover useful patterns in their data. One of these techniques - and definitely one of the most important, because it can be used for such frequent data mining tasks like classifier construction and dependence analysis - is learning graphical models from datasets of sample cases. In this paper we review the ideas underlying graphical models, with a special emphasis on the less well known possibilistic networks. We discuss the main principles of learning graphical models from data and consider briefly some algorithms that have been proposed for this task as well as data preprocessing methods and evaluation measures.