In the Anchoring Problem actions and objects must be anchored to symbols; and movement primitives as DMPs seems a good option to describe actions. In the bottom-up approach to anchoring, the recognition of an action is done applying learning techniques as clustering. Although most work done about movement recognition with DMPs is focus on weights, we propose to use the shape-attractor function as feature vector. As several DMPs formulations exist, we have analyzed the two most known to check if using the shape-attractor instead of weights is feasible for both formulations. In addition, we propose to use distance-based kernels, as RBF and TrE, to classify DMPs in some predefined actions. Our experiments based on an existing dataset and using 1-NN and SVM techniques confirm that shape-attractor function is a better choice for movement recognition with DMPs.