Neural networks are becoming popular analysis tools in spatial research, as is witnessed by various applications in recent years. The per- formance of neural network analysis needs to be carefully judged, however, since the theoretical underpinning of neuro-computing is still weakly enve- loped. In the present paper we will use the logit model as a benchmark for evaluating the result of neural network models, based on an empirical case study from Italy. The present paper aims to assess the foreseeable impact of the high-speed train in Italy, by investigating competition effects between rail and road transport modes. Two statistical models will then be com- pared, viz. the traditional logit model and a new technique for information processing, viz. the feedforward neural network model. In the study two different cases - corresponding to a different set of attributes - are investi- gated, namely by using only 'time' attributes and by using both 'time' and 'cost' attributes. From an economic viewpoint, both models appear to high- light the advantage of introducing the high-speed train system in that they show high probabilities of choosing the improved rail transport mode. The feedforward neural net model seems to provide reasonable predictions com- pared to those obtained by means of a logit model. An important lesson however, is that it is important to define properly the neural network archi- tecture and to train sufficiently the network during the learning phase.