A simple X-ray powder diffractometric (XRD) method with artificial neural networks (ANNs) for data modelling was developed to recognize and quantify two crystal modifications of ranitidine-HCl in mixtures and thus, provide information about the solid state of the bulk drug. The method was also used to quantify ranitidine-HCl from tablets in the presence of other components. An ANN consisting of three layers of neurons was trained by using a back-propagation learning rule. A sigmoid output function was used in the hidden layer to facilitate non-linear fitting. Unlike other techniques the ANN method described here employed pattern recognition on the entire XRD pattern. Correct classification was mainly influenced by the XRD pattern resolution. It was shown that data transformations improved the quantitative performance when the XRD patterns were not contaminated by other components. Only smoothed X-ray diffractograms were required to distinguish between the two crystalline forms in a mixture. In the case of ranitidine-HCl quantification from tablets, where significant interference with tablet excipients was present, better results were obtained without data transformations. The trained ANN perfectly quantified ranitidine-HCl polymorphic forms from mixtures (mean sum of squared error was less than 0.02%) and ranitidine-HCl form 1 from tablets (recovery = 98.65). Excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in pattern analysis. While the system described has been developed to interpret XRD patterns, peak detection has implications in every chemical application where the recognition of peak-shaped signals in analytical data is important. (C) 2000 Elsevier Science B.V.