This paper is concerned with the automatic recognition of German signs. The statistical approach is based on the Bayes decision rule for minimum error rate. Following speech recognition system design, which are in general based on phonemes, here the idea of an automatic sign language recognition system using subunits rather than models for whole signs will be outlined. The advantage of such a system will be a future reduction of necessary training material. Furthermore, a simplified enlargement of the existing vocabulary is expected, as new signs can be added to the vocabulary database without retrain the existing HMMs for subunits. Since it is difficult to define subunits for sign language, this approach employs totally self-organized subunits. In first experiences a recognition accuracy of 92,5% is achieved for 100 signs, which were previously trained. For 50 new signs an accuracy of 81,0% is achieved without retraining of subunit-HMMs.