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Categorizing software engineering artifacts, tasks in our case, is often a prerequisite for analysis and research. As an example, categorizing tasks according to their activity allows for a post-mortem analysis of the life cycle model of a project and can be used as a foundation for software metrics. Many categorical attributes of software artifacts are often not entered correctly or are not entered at all. For example, we observed a significant number of obsolete tasks that were not categorized as such. In this paper, we present an approach for the automatic classification of tasks in software development projects using machine learning. We evaluated our technique by two sample applications from the domain of project management: Tasks are classified according to activity and relevance, respectively. Project-relevant characteristics are learned by the classifier from the project history. Five-fold cross-validation of both applications resulted in classification accuracies of 80.51% (six categories) and 83.72% (two categories). Our approach is also applicable to other types of artifacts and categorizations within a unified software engineering model.