Peripheral pulse waveforms can become stretched and damped with increasing severity of peripheral vascular disease (PVD) and hence could provide valuable diagnostic information. This study compares the diagnostic performance of 3 established classification techniques (a linear discriminant classifier, a k-nearest neighbour classifier, and an artificial neural network) for the detection of lower limb arterial disease from pulse waveforms obtained using photoelectric plethysmography (PPG). Pulse waveforms and pre- and post-exercise Doppler ultrasound ankle to brachial pressure indices (ABPI) were obtained from patients attending a vascular measurement laboratory. A single PPG pulse from each big toe was recorded direct to computer, pre-processed, and then used as classifier input data. The correct classzer outputs were the corresponding ABPI diagnostic classification. Pulse and ABPI measurements from 100 legs were used as training data for each classifier, and the computed classifications for pulses from a further 266 legs were then compared with their ABPI diagnoses. The diagnostic accuracy of the artificial neural network (80%) was higher than for the optimized k-nearest neighbour classifier (k = 27, accuracy 76%) and the linear discriminant classifier (71 %). The Kappa measure of agreement which excludes chance was highest for the artificial neural network (57%) and significantly higher than that of the linear discriminant classifier (Kappa 40%, p < 0.05). The value of Kappa for the optimized k-nearest neighbour classifier (k = 27) was intermediate at 47%. This study has shown that classifiers can be taught to discriminate between small, and perhaps subtle, differences in features. We have demonstrated that artificial neural networks can be used to classify arterial pulse waveforms, and can perform better overall than k-nearest neighbour or linear discriminant classifiers for this application.