At present, the object categorisation literature is still dominated by the use of individual class detectors. Detect- ing multiple classes then implies the subsequent application of multiple such detectors, but such an approach is not scal- able towards high numbers of classes. This paper presents an alternative strategy, where multiple classes are detected in a combined way. This includes a decision tree approach, where ternary rather than binary nodes are used, and where nodes share features. This yields an efficient scheme, which scales much better. The paper proposes a strategy where the object samples are first distinguished from the background. Then, in a second stage, the actual object class membership of each sample is determined. The focus of the paper lies en- tirely on the first stage, i.e. the distinction from background. The tree approach for this step is compared against two al- ternative strategies, one of them being the popular cascade approach. While classification accuracy tends to be better or comparable, the speed of the proposed method is system- atically better. This advantage gets more outspoken as the number of object classes increases.