In this paper, we discuss some preliminary work on a genetic algorithmic approach to learning weighted prototypes. In this approach, a concept is represented as one or more weighted prototypes, each of which is a conjunction of weighted attribute values. In this approach, every prototype maintains its own attribute weights. A genetic algorithm is applied to generate prototypes and their attribute weights. This approach has been implemented in GABWPL and empirically evaluated on several artificial datasets.