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The mapping of the n-dimensional plasma parameter space of ASDEX Upgrade (AUG) has been performed using a 2-D self-organizing map (SOM), which reveals the map potentiality in data visualization. The proposed approach allows us the definition of simple displays capable of presenting meaningful information on the actual state of the plasma, but it also suggests to use the SOM as a disruption predictor. In this paper, various criteria have been studied to associate the risk of disruption of each cluster in the map to a disruption alarm threshold. The data for this study come from AUG experiments executed between July 2002 and November 2009. The prediction performance of the proposed system has been evaluated on a set of discharges different from those used for the map training, obtaining a good prediction success rate. A visual analysis of the predictor input signals has been performed for wrong predictions in order to identify possible common causes, and some criteria to increase prediction performance have been derived.