In the context of Statistical Pattern Recognition, one of the most important subjects, with respect to training the sample set, consists of eliminating misclassified prototypes (Editing) using distance-based methods. In this paper, an attempt to use several proximity graphs -Gabriel Graph and Relative Neighbourhood Graph- for editing the Nearest Neighbour rule is presented. Experiments on synthetic and real data have been carried out in order to investigate the recognition accuracy of the edited reference set.. A comparison with other standard editing techniques (Wilson´s editing, Holdout editing and Multiedit) is also reported.