In part 2 of this two-part series, an approach for diagnosis and quantification of stiction using a simple single-parameter model is proposed. The stiction model, in conjunction with an identified process model from routine operating data, is shown to successfully facilitate stiction diagnosis. An optimization approach is used to jointly identify the process model and the stiction parameter. This approach is based on the identification of a Hammerstein model of the system comprising the sticky valve and the process. In this work, a new identification procedure for Hammerstein systems that supports stiction diagnosis is proposed. Industrial and simulation case studies are shown to demonstrate the application of the proposed approach for diagnosing stiction.