Fault detection functions with learning method of a robotic manipulator are very useful for factory automation. All production has the possibility to fail due to unexpected accidents. To reduce the fatigue of human workers, small errors automatically should be corrected by a robot system. Also a learning method is important for fault detection, because labor of system integrator should be reduced. In this paper, an external thread fastening task by a robotic manipulator is investigated. To discriminate the four states of a task, linear support vector machine methods with two feature parameters are introduced. The effectiveness of the proposed algorithm is confirmed through an experiment and recognition examination. Finally, the ability of linear SVM is compared with artificial neural network method.