The neural network method uses ideas developed from the physiological modelling of the human brain in computational mechanics. The technique provides mechanisms analogous to biological processes such as learning from experience, generalizing from learning data to a wider set of stimuli and extraction of key attributes from excessively noisy data sets. It has found frequent application in optimization, image enhancement and pattern recognition, key problems in particle image velocimetry (PIV). The development of the method and its principal categories and features are described, with special emphasis on its application to PIV and particle tracking velocimetry (PTV). The application of the neural network method to important categories of the PIV image analysis procedure is described in the present paper. These are image enhancement, fringe analysis, PTV and stereo view reconciliation. The applications of common generic net types, feed-forwards and recurrent, are discussed and illustrated by example. The key strength of the neural technique, its ability to respond to changing circumstances by self-modification or regulation of its processing parameters, is illustrated by example and compared with conventional processing strategies adopted in PIV.