Dual-energy gamma densitometry offers a powerful technique for the non-intrusive analysis of multiphase flows. By employing multiple beam lines, information on the phase configuration can be obtained. Once the configuration is known, it then becomes possible in principle to determine the phase fractions. In practice, however, the extraction of the phase fractions from the densitometer data is complicated by the wide variety of phase configurations which can arise, and by the considerable difficulties of modelling multiphase flows. In this paper we show that neural network techniques provide a powerful approach to the analysis of data from dual-energy gamma densitometers, allowing both the phase configuration and the phase fractions to be determined with high accuracy, whilst avoiding the uncertainties associated with modelling. The technique is well suited to the determination of oil, water and gas fractions in multiphase oil pipelines. Results from linear and non-linear network models are compared, and a new technique for validating the network output is described.