Test-drives of Automated Driving Systems (ADS) generate a rich pool of data that can be used to analyze and improve the ADS software stack. In this context, disengagement events, i.e., situations where the safety driver takes over control of the ADS are of specific interest. While it is easy to au-tomatically identify when a disengagement happens, it is a non-trivial, and therefore manually performed task to classify disengagements with regards to its cause. The goal of our study was to replace the current manual classi-fication process with a more efficient, scalable and reliable automatic ap-proach. To this end, using supervised learning, we developed and tested a set of eight CNN-based binary classifiers, one for each label type. The eval-uation indicated a high performance for six of the eight label types. A fol-low-up SHAP analysis gave insights into the reasons for the good perfor-mance of the classifiers.