As the amount of online information in the form of web pages grows, the demand of text categorization to assist in efficient retrieval will surely increase in tandem. Even though such a task may be performed manually by the domain experts, it is unlikely that such human categorization will be able to keep pace with the tremendous growth of the World Wide Web (WWW). The sheer volume of data that is available on the Internet will surely overwhelm these human categorizers. Hence, as the Internet expands, the importance of having this process automated will become increasingly important. Many clustering techniques have been commonly used to retrieve, filter, and categorize documents. The traditional clustering algorithms either use a priori knowledge of document structures to define a distance or similarity among these documents, or use probabilistic techniques such as Bayesian classification. In recent studies, several species of ants have been observed to exhibit collective behavior that has inspired the computation algorithm commonly known as swarm intelligence. This has been used in a variety of different applications, ranging from exploratory data analysis, scheduling, to graph partitioning. This paper investigates the use of an ant-based algorithm to classify English Web.