In this paper, the authors focus on real-world applications of fuzzy techniques for data mining. It gives a presentation of the theoretical background common to all applications, lying on two main elements: the concept of similarity and the fuzzy machine learning framework. It then describes a panel of real-world applications covering several domains namely medical, educational, chemical and multimedia. There are two main types of uncertainty in supervised learning: statistical and cognitive. Statistical uncertainty deals with the random behavior of nature and all existing data mining techniques can handle the uncertainty that arises in the natural world from statistical variations or randomness. Cognitive uncertainty, on the other hand, deals with human cognition.