This paper describes a Web filtering system “WebGuard,” which aims to automatically detect and filter adult content on the Web. WebGuard uses data mining techniques to classify URLs into two classes: suspect URLs and normal URLs. The suspect URLs are stored in a database, which is constantly and automatically updated in order to reflect the highly dynamic evolution of the Web. When working, WebGuard simply captures a user’s URL, matches it with the suspect URLs stored in the database and takes an appropriate action — filtering or blocking — according to the result of the analysis. We started out with a study of most existing software so as to get to know the possibilities and functionalities available on the market at the moment. This phase enabled us to better evaluate the performances of our product as it was being developed. Thus, the second phase of our work was devoted to research into the usual algorithms regarding their advantages and drawbacks. Having gathered this knowledge, we are currently implementing a system that will combine several algorithms in order to increase the software’s performance. Our preliminary results show that it can detect and filter adult content effectively.