Various practical systems capable of extracting descriptive decision-making knowledge from data have been developed and evaluated. Techniques that represent knowledge about classified tasks in the form of decision trees are examined. A sample of techniques is sketched, ranging from basic methods of constructing decision trees to ways of using them noncategorically. Some characteristics that suggest whether a particular classification task is likely to be amenable or not to tree-based methods are discussed. >