A significant number of control loops in process plants perform poorly due to control valve stiction. Stiction in control valves is the most common and long standing problem in industry, resulting in oscillations in process variables which subsequently lowers product quality and productivity. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to the plant operations. In this paper, nonlinear principal component analysis (NLPCA), which is widely known for its capability in unravelling nonlinear correlations in process data, is extended to investigate control valve stiction problems. Results from simulated case studies show that NLPCA is a promising tool for valve stiction diagnosis.