D.Phil. / The Semantic Web is an extension of the current Web. The goal of the Semantic Web is to give information “well-defined meaning, enabling computers and people to work in better cooperation” (Berners-Lee, Hendler, & Lassila, 2001). While the Semantic Web is not artificial intelligence, it does involve defining information in such a way that it can be more easily “understood” by machines. The Semantic Web builds upon the advantages offered by XML, and introduces languages such as the Resource Description Framework to address some of the shortcomings of XML. It uses ontologies to provide a mechanism for information processing on the Web. Object recognition involves the recognition of unknown objects and is usually divided into two types of recognition: object classification and object identification. Classification refers to the categorization of an unknown object into a known group, while identification is the matching of an unknown object against the memory of a known object. Most object recognition techniques, regardless of the recognition type, involve the extraction of some type of processable data from objects, and the subsequent comparison of the extracted information. The research presented in this thesis investigates the possibility of using the languages developed for the Semantic Web to perform some type of object recognition. It is hoped that by treating object recognition as an information management task, the advantages provided by the information-centric Semantic Web can be used in good stead. The goal of the research is to determine whether ontology-based descriptions can be created, whether such descriptions can be compared, and to what extent the use of the Semantic Web could enhance information sharing in object recognition. In order to investigate these questions, the research defines the Semantic Web Object Recognition Model. The model provides a recognition framework that uses ontologies to create and compare object descriptions. The model also suggests the use of web agents to perform distributed object comparisons across the relevant domain.