This paper addresses the connectionist accounts of two related topics, the representation of ‘type hierarchies’ and ‘cognitive categories’ (as the terms are used in knowledge representation research and cognitive psychology respectively). There are important problems with the current accounts of these topics, particularly the circularity of the definition of subvector type representations, and the inaccessibility to the network of prototype category representations. We introduce our own account of type and category, based on the identification of the ‘domain’ of units on which inputs of the same category are typically represented. Domains are formed by learning information about the co-activation of units in a population, and identified (for a given input by using this information to generate a ‘centrality’ value for each unit (collectively called a ‘centrality distribution’). The main claims of this paper are that centrality distributions can be used as representations of categories, and can be hierarchically organized to capture type information. We illustrate these claims with implementations, and discuss the representation of category gradedness, and mechanisms of property inheritance. The use of centrality distributions (as well (as patterns of activation)as representations is an extension to basic connectionist methods.