Along with the ever-growing Web comes the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering undesirable Web content. In this paper, we investigate this problem and describe WebGuard, an automatic machine learning-based pornographic Web site classification and filtering system. Unlike most commercial filtering products, which are mainly based on textual content-based analysis such as indicative keywords detection or manually collected black list checking, WebGuard relies on several major data mining techniques associated with textual, structural content-based analysis, and skin color related visual content-based analysis as well. Experiments conducted on a testbed of 400 Web sites including 200 adult sites and 200 nonpornographic ones showed WebGuard's filtering effectiveness, reaching a 97.4 percent classification accuracy rate when textual and structural content-based analysis was combined with visual content-based analysis. Further experiments on a black list of 12,311 adult Web sites manually collected and classified by the French Ministry of Education showed that WebGuard scored a 95.62 percent classification accuracy rate. The basic framework of WebGuard can apply to other categorization problems of Web sites which combine, as most of them do today, textual and visual content.