This work outlines the development of a fault diagnostic system to supervise a desalination plant MSF during dynamic states (e.g., start-up and changes of operative conditions) using a real-time expert system. This diagnostic system processes the plant data to determine whether the process state during a dynamic state is normal or not. In the last case, the diagnostic system determines the cause of the abnormal process state. The first step is the determination of the potential faults (equipment malfunctions or operator mistakes). This set contains all the faults the diagnostic system will be able to recognise. Next, to improve the diagnostic system performance, a careful selection of the plant sensors to be considered 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 then used to generate IF-THEN rules to build the knowledge base. During the diagnostic system operation, at each sampled time, the lectures 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 1 for each potential fault. This number represents the security of how the corresponding fault is affecting the plant. The higher this value, the higher the security of the affirmation. Finally, a dynamic simulator was used to evaluate the performance of the diagnostic system.