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) [2 ] and the Multimodal Neighbourhood signature (MNS) [7 ] 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.