This work outlines the development of a fault diagnostic system for a multi-stage flash (MSF)desalination plant MSF using a real time expert system. This diagnostic system processes the plant data to determine whether the process state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal state. The first step is to determinate the potential faults. This set contains all the faults the diagnostic system should be able to recognize. Then, to improve the diagnostic system performance, a careful selection of the plant sensors that will be supervised by the diagnostic system is done. The knowledge base of the expert system is automatically obtained from a qualitative model of the plant. The qualitative model is a signed directed graph (SDG). The SDG is used by a qualitative simulator to forecast, for each potential fault, the possible qualitative evolutions of the plant. This information is used to generate rules 'if-then' to build the knowledge base. During the diagnostic system operation, at each sampling time, the readings of the previously selected sensors are transformed in qualitative values. These values are used by the expert system to evaluate the rules by using fuzzy logic. The result is an index between 0 and I for each potential fault. This number represents the certainty about the corresponding fault is affecting the plant. The higher is the value, the higher is the certainty of that affirmation. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.