An artificial neural network (ANN) was trained to classify photoelectric plethysmographic (PPG) pulse waveforms for the diagnosis of lower limb peripheral vascular disease (PVD). PPG pulses from the lower limbs, and pre- and post-exercise Doppler ultrasound ankle to brachial systolic blood pressure ratio measurements were obtained from patients referred to a vascular investigation laboratory. A single PPG pulse from the big toe of each leg was processed and normalized, and used as input data to the ANN. The ANN outputs represented the diagnostic classifications (normal, significant PVD and major PVD) and the ANN was trained with the ankle to brachial pressure indices (ABPI). The ANN structure consisted of an input layer (50 neuron units from the PPG pulse input), a single hidden layer (15 neurons) and an output layer (3 neurons for the PVD diagnoses). The back-propagation learning algorithm was used to train the ANN for 500 epochs with a PPG training set of pulses from 100 legs. Test data for network assessment comprised pulses from a further 50 legs (20 normal and 30 PVD, of which 15 were categorized as having major disease). A network sensitivity of 93% and specificity of 85% was achieved with respect to the Doppler ABPI standard, giving a diagnostic accuracy of 90%. The results of this study indicate that a neural network can be trained to distinguish between PPG pulses from normal and diseased lower limb arteries. The simplicity of PPG measurement and neural network classification holds promise for the screening of lower limb arterial PVD.