The Basel Committee on Banking Supervision proposes a capital adequacy framework that allows banks to calculate capital requirement for their banking books using internal assessments of key risk drivers. Hence the need for systems to assess credit risk. Among the new methods, artificial neural networks have shown promising results. In this work, we describe the case of a successful application of neural networks to credit risk assessment. We developed two neural network systems, one with a standard feedforward network, while the other with a special purpose architecture. The application is tested on real-world data, related to Italian small businesses. We show that neural networks can be very successful in learning and estimating the in bonis/ default tendency of a borrower, provided that careful data analysis, data pre-processing and training are performed.