In this study, a neural network is used to model the complex relationships associated with dc plasma spraying. The paper describes how training and test data were experimentally obtained for alumina-titania particles processed under different conditions in order to assess various learning approaches and the predictive ability of the model. As reported, 80% of the test database was successfully recognized and the misclassifications are the result of not having enough data sets to adequately cover the wide range of values obtained during the experiments. This work considers in-flight particle characteristics as a first step of a more global approach that includes coating microstructure and mechanical properties as well. Paper includes a German-language abstract.