The datasets that are part of the Linking Open Data cloud diagram (LOD cloud) are classified into the following topical categories: media, government, publications, life sciences, geographic, social networking, user-generated content, and cross-domain. The topical categories were manually assigned to the datasets. In this paper, we investigate to which extent the topical classification of new LOD datasets can be automated using machine learning techniques and the existing annotations as supervision. We conducted experiments with different classification techniques and different feature sets. The best classification technique/feature set combination reaches an accuracy of 81.62% on the task of assigning one out of the eight classes to a given LOD dataset. A deeper inspection of the classification errors reveals problems with the manual classification of datasets in the current LOD cloud.