The use of numerical modeling in the development and optimization of thermal spray processes has been hampered by the complexities of the process, with up to 50 interdependent variables involved, and the need to identify and explicitly define each relationship. This work demonstrates an alternative modeling approach that employs neural network learning algorithms. Plasma spraying was selected as the test case because it presents the greatest challenge in terms of processing parameters and the magnitude of their effect on layer quality. This paper provides an introduction to neural networks and its use in thermal spraying. A companion paper in these same proceedings presents an example in which the method is used to link processing parameters with in-flight particle characteristics in a dc plasma jet. Paper includes a German-language abstract.