AIMS: The efficacy of an artificial intelligence technique, neural network analysis, was examined in differentiating two optic neuropathies with overlapping clinical profiles-idiopathic optic neuritis (ON) and non-arteritic anterior ischaemic optic neuropathy (AION). METHODS: A neural network was trained with data from 116 patients with 'gold standard' diagnoses of ON or AION. It was then tested with data from 128 patients with presumed ON or AION, and the correlation of the network's diagnosis with that of expert clinicians tabulated. RESULTS: The network agreed with the clinicians on 97.8% (88 of 90) of the patients with presumed ON and 94.7% (36 of 38) of the patients with presumed AION. Youth, female sex, better initial acuity, a central scotoma, subsequent improvement in acuity, or progressive disease biased the network towards a diagnosis of ON, while advanced age, male sex, presence of hypertension, poor initial acuity, an altitudinal field defect, disc oedema, or less improvement in acuity biased the network towards a diagnosis of AION. CONCLUSION: Neural network analysis is a useful technique for classification of optic neuropathies, particularly where there is overlap of clinical findings.