Disturbance and fault diagnosis techniques that rely on statistical methods traditionally utilize distance based discrimination functions. Complementary information is contained in the angular relations between data clusters representing process operations under various disturbances. A novel disturbance diagnosis approach is presented based on angle discriminants. The diagnosis method is successful in cases where distance based discrimination is not very accurate. The methodology is illustrated by diagnosing various disturbances in the Tennessee Eastman process and compared with the diagnosis utilizing distance based algorithms.