A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as always, and distance based, such as the euclidean distance. Special purpose techniques are presented that allow these predicates to be handled eficiently when performing top-down induction. Furthermore, by employing boosting, the accuracy of the resulting classifiers can be improved significantly. Experiments on several different datasets show that the proposed method is highly competitive with previous approaches.