In current aircraft engines challenging coatings or coating systems with different functions are used. Many of these coatings are applied to different components with thermal spray processes. Thermal spraying is a very sensitive and very complex process, which is influenced by numerous controllable variables like the powder feed rate, the gas flow rates, etc. as well as not controllable variables like the torch wear, varying powder properties, etc. With conventional process control based on linear algorithms it is not possible to enduringly create constant coating properties, because they cannot describe the complexity of all influencing variables. In this work, the possibility of a closed loop was investigated exemplarily for an atmospheric plasma spray process (APS). During serial production a data base was collected, consisting information about torch and plume conditions as well as powder and coating properties. This data base was used to train different neural networks (NN). With regard to the automation of the APS, the NN obtained target values of relevant coating properties and should calculate the needed control variables. The result of this work shows the difficulties in the quantification of relevant influencing variables and the feasibility of the plasma spray process control with neural network.