A system is proposed which learns spatial representatious of planar feature point sets under supervised learning. A key (Wewtgessing3 aspect of the learning system is the transformarion of the each "static" point set instance into a "dynamic" set of measm'es of sp~tisd relationships spread out over time. A morphologically based wave propagation algorithm [1, 2, 3] performs this transformation of Sl~tla' structm- e into temporal structure. The learning system  ls based upon classifiers using bucket brigade and genetic algorithms  to respectively modify strengths and create new classifier rules. Such learning systems are designed to exploit temporal reg-larities in learning environments and, thus, fit well with the wave Ixopagntion preprocessing. An initial test environment is proposed that attempts to re-label arbitrary feature labels into structurally memfingful labels.