Automatic or semi-automatic categorization of items (e.g. documents) into a taxonomy is an important and challenging machine-learning task. In this paper, we present a module for semi-automatic categorization of video-recorded lectures. Properly categorized lectures provide the user with a better browsing experience which makes her more efficient in accessing the desired content. Our categorizer combines information found in texts associated with lectures and information extracted from various links between lectures in a unified machine-learning framework. By taking not only texts but also the links into account, the classification accuracy is increased by 12–20%.