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In this paper we address the problem of object recognition from 2D views. A new approach is proposed which combines the recognition systems based on attribute relational graph matching (ARG) and the multimodal neighbourhood signature (MNS) method. In the new system we use the MNS method as a pre-matching stage to prune the number of model candidates. The ARG method then identifies the best model among the candidates through a relaxation labelling process. The results of experiments show a considerable gain in the ARG matching speed. Interestingly, as a result of the reduction in the entropy of labelling by a virtue model pruning, the recognition rate for extreme object views also improves.