The application of a genetic reservoir characterisation concept to the calculation of petrophysical properties requires the prediction of lithofacies followed by the assignment of petrophysical properties, according to the specific lithofacies predicted. Common classification methods which fulfil this task include discriminant analysis and backpropagation neural networks. While discriminant analysis is a well-established statistical classification method, backpropagation neural networks are relatively new, and their performance in predicting lithofacies, porosity and permeability, when compared to discriminant analysis, has not been widely studied. This work compares the performance of these two methods in prediction of reservoir properties by considering log and cove data from a shaly glauconitic reservoir. The neural network approach while subject to a degree of trial and error as regards the selection of the optimum configuration of middle nodes, is shown to be capable of excellent performance. In the example problem considered the neural network approach provided estimates superior to those based on a discriminant analysis approach. Further studies, on different formations, will be required to test the generality of this conclusion, and to refine the selection of neural network parameters.