With the proliferation of harmful Internet content such as pornography, violence, and hate messages, effective content-filtering systems are essential. Many Web-filtering systems are commercially available, and potential users can download trial versions from the Internet. However, the techniques these systems use are insufficiently accurate and do not adapt well to the ever-changing Web. To solve this problem, we propose using artificial neural networks to classify Web pages during content filtering. We focus on blocking pornography because it is among the most prolific and harmful Web content. However, our general framework is adaptable for filtering other objectionable Web material.