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A great challange in robotics is to make robots more like humans. One important aspect is to make robots move like humans and recognize their motions. Both tasks are based on human motion trajectories and require a proper modelling. To accomplish these tasks, we acquire data from complex motions like setting the table, pouring water into a cup, or stirring the content of the cup. The objectives of our studies are to identify the subject doing the motion and to detect slight changes in motion constraints with automatic classification methods. For that purpose, we present reliable methods based on Elman networks and hidden Markov models. We develop the model parameters and compare the performance of the methods, especially, the classification results and the suitability for the classification tasks.