In the last years there has been a growing interest on neural approaches to disruption prediction. The drawback of these approaches is that the system performance could deteriorate once it is on-line. This could be the case for a disruption predictor for JET, where new plasma configurations might present features completely different from those observed in the experiments used during the training phase. This 'novelty' can lead to incorrect behaviour of the network. A Novelty Detection method, which determines the novelty of the input of the prediction system, can be used to assess the network reliability. This paper presents a Support Vector Machines disruption predictor for JET, wherein multiple plasma diagnostic signals are combined to provide a composite impending disruption warning indicator. In a Support Vector Machine the analysis of the decision function value gives useful information about the novelty of an input and, on the reliability of the predictor output, during on-line applications. Results show the suitability of Support Vector Machines both for prediction and novelty detection tasks at JET.