In simplistic terms the psychological literature on concept representation currently tends to favour either prototype or exemplar-based theories. Similar distinctions can be found within the computational literature. This paper examines the predictions of two such models in the domain of French gender attribution. The comparison between a standard feed-forward neural net model (prototype) and Analogical Modeling (exemplar) is of interest, aside from the broad issues of conceptual representation, since arguments have been presented by proponents of the latter theory as to its theoretical superiority over connectionist models on a variety of grounds. These claims are argued here to be either mistaken or less than compelling. It is, therefore, of some value to evaluate the two models on purely empirical grounds. A variety of simulations using a range of different representations and databases are compared with respect to their ability to generalise gender classifications to a set of unknown French nouns. Analysis shows that both models produce results which are not statistically significantly different.