Artificial intelligence methods such as artificial neural networks, Bayesian networks, genetic algorithms, and others, have shown great potential for application, not only as classification schemes, but also in numerical data analysis. In this work, we explore how, from a limited number of spectra (around 200), an ANN could be efficiently developed, using data augmentation techniques and optimized architecture, and used to analyse neutron activation analysis (NAA) data. The IAEA Collaborating Centre Research Institute Delft (RID), Netherlands, has collected NAA data sets consisting of one single spectrum per sample to determine one single element (selenium), with addition of a marker (caesium) for flux normalization, all irradiated and measured the exact same way and analysed with k0-based software. The problem studied is one of the simplest that can be addressed with NAA; therefore the present work is intended merely as proof of concept that ANNs can perform well in NAA data analysis of simple problems. We present the results and discuss how to extend the present work to more demanding problems in NAA.