There are millions of home pages on the web. Each page contains valuable data about the page’s owner that can be used for marketing purposes. These pages have to be classified according to interests. The traditional Information Retrieval approach requires large training sets that are classified by human experts. Knowledge-based methods, which use handcrafted rules, require a significant investment to develop the rule base. Both these approaches are very time consuming. We are using glossaries, which are freely available on the Internet, to determine interests from home pages. Processing of these glossaries can be automated and requires little human effort and time, compared to the other two approaches. Once the terms have been extracted from these glossaries, they can be used to infer interests from the home pages of web users. This paper describes the system we have developed for classifying home pages by interests. On an experiment of 400 pages, we found that the glossary with the highest number of word matches is the correct interest in 44.75% of the pages. The correct interest is in the top three highest returned interests in 72.25% of the pages, and the correct interest is in the top five returned interest matches in 84.5% of the pages.1.