Previous investigations of thermal spraying processes have shown that the relations between process parameters and the objective measurements are very complex. An approved approach to control complex processes is the application of Neural Networks (NN). Thus, Neural Networks have been designed to control the process of APS and HVOF spraying. Feed forward Neural Networks (Multi Layer Perceptron, MLP) are used. They are able to control a process. The way to train the Neural Network is to conduct as many experiments as possible, this is the major difficulty for the industrial use of Neural Networks. To save time and money DoE (Design of Experiments) is used to create an optimal experimental plan for the training. For testing the implementation of Neural Networks coatings are sprayed with APS, using DoE. The Neural Networks are combined with the particle flux imaging (PFI) tool. In future this combination will be able to provide an open loop control for thermal spray processes. The Neural Networks will be integrated with the software of the PFI-unit in order to create an easy to handle and affordable process control device. First experiments have been done with the APS process by spraying ZrO2 onto steel substrates. Afterwards the porosity of the coating was correlated to the recorded images and to the process parameters.