Lithofacies definition in the subsurface is an important factor in modelling, regardless of the scale being on the reservoir or basin level. In areas with low exploration level, modelling of lithofacies distribution presents a complicated task as very few inputs are available. For this purpose, a case study in Požega Valley was selected with only one existing well and several seismic sections on an area covering roughly 850 km2. For the task of expanding the input dataset for lithofacies modelling, neural network analysis was performed which incorporated interpreted lithofacies (sandstone, siltite, marl and breccia-conglomerate) in a single well and attribute data gathered from a seismic section. Three types of different neural networks were used for the analysis- multi-layer perceptron, radial-basis function and probabilistic neural network. As a result, three lithofacies models were built alongside a seismic section based upon predictions acquired from the neural networks. Three lithofacies were successfully predicted on the section while the breccia- conglomerate was either missing or under predicted and mostly positioned in geologically invalid interval. Results obtained by single networks differed from one another which pointed out that a result from a single network should not be treated as representative, thus the facies distribution for modelling should be acquired from either an ensemble of neural networks or several neural networks. Analysis showed the initial potential of the usability of neural networks and seismic attribute analysis on vintage seismic sections with pointed out possible drawbacks of the applications.