Human subjects are able to identify the sex of faces with very high accuracy. Using photographs of adults in which hair was concealed by a swimming cap, subjects performed with 96% accuracy. Previous work has identified a number of dimensions on which the faces of men and women differ. An attempt to combine these dimensions into a single function to classify male and female faces reliably is described. Photographs were taken of 91 male and 88 female faces in full face and profile. These were measured in several ways: (i) simple distances between key points in the pictures; (ii) ratios and angles formed between key points in the pictures; (iii) three-dimensional (3-D) distances derived by combination of full-face and profile photographs. Discriminant function analysis showed that the best discriminators were derived from simple distance measurements in the full face (85% accuracy with 12 variables) and 3-D distances (85% accuracy with 6 variables). Combining measures taken from the picture plane with those derived in 3-D produced a discriminator approaching human performance (94% accuracy with 16 variables). Performance of the discriminant function was compared with that of human perceivers and found to be correlated, but far from perfectly. The difficulty of deriving a reliable function to distinguish between the sexes is discussed with reference to the development of automatic face-processing programs in machine vision. It is argued that such systems will need to incorporate an understanding of the stimuli if they are to be effective.