Over the past two decades. Artificial Neural Network (ANN) techniques have been used in many fields of research, due to their high speed and improved robustness and failure tolerance capabilities compared with conventional modelling approaches. In applications such as railway vehicle dynamics, discrete scenarios of vehicle/track interactions are currently modelled using computer packages such as Simpack, ADAMS/Rail, Vampire or Medyna, which use multi-body techniques to accurately model different aspects of rail vehicles and tracks such as derailment or passenger comfort. The authors have developed new ANN techniques, which make it possible to achieve ANN model accuracies comparable to those of multi-body techniques. A particular ANN structure has been designed with the aim of simplifying the training of ANNs with long training data sets. This is a Recurrent Neural Network (RNN) structure characterised by an optimised feedback technique, which requires very little computational power. This novel structure and other more conventional RNN structures have been trained and tested and the processing times compared. The efficiency of the novel ANN structure in modelling a number of vehicle types, from passenger to friction damped freight vehicles, has been validated against commonly used techniques and also with a newly designed method, which consists of a combination of statistical functions applied to assess different aspects of the ANN models responses. The novel ANN structure appears to be much faster than other structures commonly used for non-linear system modelling and adequate for the purposes of rail vehicle modelling. Compared to conventional ANN validation techniques, such as the mean square error and the cross-correlation function analysis, the novel assessment technique results in a more accurate quantification of the error terms and therefore, in a safer assessment, which may be focused on aspects of the ANN model responses which are relevant in the context of railway engineering. It is possible that this novel approach to designing efficient ANNs could be applied to a wide variety of scientific fields involved in the application of ANN techniques.