Abstract: An adaptive fault identification system identifies repeat faults by capturing data representative of a fault. The captured data is subjected to a search tool and compared to data that is known to represent faults, and if possible, the unknown fault is identified. The search tool does not need to exactly match the data representing the unknown fault to the stored data. Instead, if the recall data pattern is similar enough to some of the stored data, a list of labels is returned where each label points to a recommended corrective action. By employing a learning algorithm, the similarity comparisons can be adapted to reflect greater knowledge of faults so that more accurate similarity comparisons can be made.