Disruptions are mayor instabilities and remain one of the main problems in Tokamaks' operation. Using Joint European Torus (JET) database, a disruption predictor is developed using exploratory data analysis methods and supervised learning techniques. The main objective of the work consists of determining how much in advance of the disruption a classifier can operate with acceptable success rate.