The diagnostic performance of an artificial neural network pulse classification system for the detection of peripheral vascular disease was investigated prospectively. Lower limb photoelectric plethysmographic pulses, and Doppler ankle/brachial pressure index (ABPI) measurements (pre- and post-exercise) were obtained from 200 patients referred to a vascular investigation laboratory. A single toe pulse was processed and used as input data to a neural network which had been trained previously with a set of pulses from 100 legs. The neural network outputs represented the diagnostic arterial disease classifications defined by the ABPI. From the 200 patients entered prospectively, 266 legs were available for neural network assessment. A network sensitivity of 92% and specificity of 63% were achieved with a diagnostic accuracy of 80%. By using a higher confidence for the classification decision a small, but insignificant overall improvement was obtained. When a borderline classification was introduced 100% sensitivity and 100% negative predictive value were obtained, though 31% of legs were unclassifiable. Nevertheless, the very high sensitivity and negative predictive value could make this quick and simple technique the one of choice for the first stage in screening large numbers of subjects.