During plasma spray process, many intrinsic operating parameters allow tailoring the in-flight particle characteristics (temperature and velocity), thus affecting the final coating characteristics. Among them, plasma enthalpy, thermal conductivity, momentum, density, etc. result from the selection of extrinsic operating parameters such as the plasma torch nozzle geometry, composition and flow rate of plasma forming gases, the arc current intensity, etc. The complex relationships among those operating parameters make it difficult to fully predict their effects. Moreover, temporal fluctuations (anode wear for example) require "real time" corrections to maintain particle characteristics to targeted values. In addition, substrate temperature has to be maintained to targeted values depending upon the feedstock to be sprayed, the geometry of the part to be coated, its thermal capacity, etc. An expert system was built to optimize and control some of the main extrinsic operating parameters. This expert system includes two parts: 1) an artificial neural network (ANN), which predicts an extrinsic operating window and 2) a fuzzy logic controller (FLC) to control it. The paper details the general architecture of the system, discusses its limits and typical characteristics. An example is finally presented.