In developing a Fuzzy Logic Controller, the establishment of boundaries between adjacent fuzzy sets of the function that characterizes these boundaries is a formidable task. Considerable re-examination of the various process state combinations is necessary to ensure that the output distribution changes in a reliable and consistent manner. Rule-based production systems that depend on fuzzy logic can produce vacuums and valleys in the output distributions. These deficiencies must be eliminated. An Expert System has been developed to adjust the boundary conditions of a two-variable fuzzy logic control system using a feedback learning procedure. Membership values are recalculated on each cycle to ensure smooth transfer of output belief from one rule to another as the process states change. This method is performed whenever the confidence level of the definition of a specific linguistic expression is adjusted. The application of this controller to supervice a secondary crushing plant simulator is described.