We consider the problem of automatic construction of qualitative models by inductive learning from quantitative examples. We present an algorithm QUIN (QUalitative INduction) that learns qualitative trees from a set of examples described with numerical attributes. At difference with decision trees, the leaves of qualitative trees contain qualitative functional constraints as used in qualitative reasoning. A qualitative tree defines a partition of the attribute space into the areas with common qualitative behaviour of the chosen class variable.