Inductive Logic Programming is mainly concerned with the problem of learning concept definitions from positive and negative examples of these concepts and background knowledge. Because of complexity problems, the underlying first order language is often restricted to variables, predicates and constants. In this paper, we propose a new approach for learning logic programs containing function symbols other than constants. The underlying idea is to consider a domain that enables to interpret the function symbols and to compute the interest of a given value for discriminating positive and negative examples. This is modelized in the framework of Constraint Logic Programming and the algorithm that we propose enables to learn some constraint logic programs. This algorithm has been implemented in the system ICC. In order to reduce the complexity, biases have been introduced, as for instance the form of constraints that can be learned, the depth of a term or the size of the constraints.