Twitter is a widely used online social networking site where users post short messages limited to 140 characters. The small length of these messages is a challenge when it comes to classifying them into categories. In this paper we propose a system that automatically classifies Twitter messages into a set of predefined categories. The system takes into account not only the tweet text, but also external features such as words from linked URLs, mentioned user profiles, and Wikipedia articles. The system is evaluated using various combinations of feature sets. According to our results, the combination of feature sets that achieves the highest accuracy of 90.8% is when the original tweet terms are combined with user profile terms along with terms extracted from linked URLs. Including terms from Wikipedia pages, found specifically for each tweet, is shown to decrease accuracy for the original test set, however accuracy was shown to increase using a fraction of the original test set containing only tweets without URLs.