Object recognition and categorization is a fundamental aspect of cognition in humans and animals. Models have been implemented around the idea that categories are sets of frequently co-occurring features. Out of these models a question has been raised, namely what is the mechanism by which we learn a hierarchically organized set of categories, including types and subtypes? In this paper we introduce such a model, the Dominant Property Assembly Network (DPAN). DPAN uses an unsupervised neural network to model an agent which develops a hierarchy of object categories based on highly correlated object features. Initially, the network generates representations of high-level object types by identifying commonly co-occurring sets of features. Over time, the network will start to use an inhibition of return (IOR) operation to examine the features of a categorized object that make it unusual as an instance of its identified category.