AbstractThe concept and methodology of artificial neural networks is introduced. Like pattern recognition, the techniques can be classified as supervised (requiringa prioriknowledge of class membership) and unsupervised (making no assumptions about class membership). An unsupervised neural network method, Kohonen Topology‐Preserving Mapping, is applied to a wide matrix of physicochemical property data for a set of antifilarial antimycin analogues containing structural outliers. Principal component analysis failed to give a good 2D representation of the data set as a whole due to linear constraints in the model which gave undue influence to the outliers. Kohonen mapping compared favourably with non‐linear unsupervised statistical pattern recognition methods for 2D representation of compound similarity and for classification based on antifilarial activity. It may prove a valuable technique for QSAR in situations where a linear method does not model the data well and a high throughput of test compounds is indica