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In this paper we propose a string-based approach to effectively represent trajectories in the 3D space. The strategy is coupled with a syntactical matching algorithm that allows evaluating the similarity of the retrieved data with pre-stored templates. The symbolic representation of the trajectory, is the core of the proposed system, which helps discriminating among different tracks using a modified version of the edit-distance. The hierarchical application of the algorithm on the spatial and temporal components helps detecting anomalous trajectories, and has proven to be robust in automatically learning new instances or classes of paths. We present the results achieved by performing a number of tests in an indoor lab used as a testbed for assisted living applications. The algorithm can discriminate among different classes of trajectories and can recognize actions and detect anomalies within the same class.