This project includes the study and development of an end of line tester diagnosis system. The inputs are torque signals from the EOLT and the outputs are the current states of the machine. A raw data study is implemented in order to extract the best features for the posterior evaluation. The signal is partitioned, filtered and analyzed, therefore, some statistical features are calculated. Subsequently two different parts are developed, the novelty detection and the classification. The first one allows the system to recognize if the input data comes from a new machine state, never seen before, and the second classifies the input data into its target. For the novelty system, a subset of features is selected and the novelty detection algorithms are applied. The selection is based on the healthy state data in order to recognize any other state as faulty. The algorithm relearns with every new state encountered for the purpose of detecting only as new patterns the ones that have never seen before. In the other hand, for the classification, the features are selected and extracted with every new data batch detected, using the healthy state variables and the other from the faulty states. The algorithm classify the input data and it is retrained with every new batch. The solution is implemented with a user interface allowing the exemplification of all the steps and the labeling of new patterns encountered