In the nuclear sciences and radiation detection fields, the differentiation between gamma-ray and neutron interactions inside a detector volume continues to be an area of active research. Historically, the primary mechanism for conducting particle identification has been Pulse Shape Discrimination (PSD). However, almost all variations of this technique rely on only two factors: the area of the tail, and the total area of the pulse. Within the last decade the emergence of advanced machine learning techniques, most specifically Artificial Neural Networks (ANN), offers a unique opportunity to capitalize on the entirety of the waveform. But such techniques appear highly reliant on the quality of data sets used for training. Our research addresses this challenge to quantify the relative performances of networks trained on a variety of data sets and subjected to the same test. Furthermore, we offer an analysis of the portability of a network trained on one detector to a similar detector.