In this paper, we make an attempt to use Inductive Logic Programming (ILP) to automatically learn non trivial de- scriptions of symbols, based on a formal description. This work is a first step in this direction and is rather a proof of concept, rather than a fully operational and robust frame- work. The overall goal of our approach is to express graphic symbols by a number of primitives that may be of any com- plexity (i.e. not necessarily just lines or points) and con- necting relationships that can be deduced from straightfor- ward state-of-the art image treatment and analysis tools. This representation is then used as an input to an ILP solver, in order to deduce non obvious characteristics that may lead to a more semantic related recognition process.