Abstract Software systems are increasingly being used in business or mission critical scenarios, where the presence of certain types of software defects, i.e., bugs, may result in catastrophic consequences (e.g., financial losses or even the loss of human lives). To deploy systems in which we can rely on, it is vital to understand the types of defects that tend to affect such systems. This allows developers to take proper action, such as adapting the development process or redirecting testing efforts (e.g., using a certain set of testing techniques, or focusing on certain parts of the system). Orthogonal Defect Classification (ODC) has emerged as a popular method for classifying software defects, but it requires one or more experts to categorize each defect in a quite complex and time-consuming process. In this paper, we evaluate the use of machine learning algorithms (k-Nearest Neighbors, Support Vector Machines, Naive Bayes, Nearest Centroid, Random Forest and Recurrent Neural Networks) for automatic classification of software defects using ODC, based on unstructured textual bug reports. Experimental results reveal the difficulties in automatically classifying certain ODC attributes solely using reports, but also suggest that the overall classification accuracy may be improved in most of the cases, if larger datasets are used.