Control performance monitoring is an important technology to keep highly efficient operation of production plants. Bad control performance is caused mainly by inadequate controller tuning or equipment malfunction. Valve Stiction is the most common problem in pneumatic control valves, which are widely used in the process industry. Stiction causes persistent fluctuation of process variables. Therefore, developing a method to detect stiction and distinguish it from inadequate controller tuning is crucial to help operators take an appropriate action for improving control performance. In the present work, valve stiction is modeled by taking into account its physical mechanism, and then new stiction detection algorithms are proposed. The usefulness of the proposed detection methods are demonstrated by comparing them with conventional methods. It is shown that the proposed methods can successfully detect valve stiction, distinguish it from bad tuning or disturbances, and quantify the degree of stiction, by using simulation data sets and real operation data sets obtained from several chemical processes.