The contribution of aroma to the flavour of Cheddar cheese has been investigated using 40 samples of diverse origin, including cheese made from raw milk. A panel of 15 trained assessors rated the cheese for 9 aroma attributes and for 10 flavour descriptors. The attributes chosen discriminated between samples. The prediction, by linear regression (LR), principal component regression (PCR) and partial least squares regression (PLS1), of individual flavour ratings from the aroma of the cheese was compared. PCR and PLS1 predicted flavour with slightly greater certainty than did LR. A further improvement in predictive power was achieved by post-processing of PCR and PLS1 scores using an artificial neural network. Despite the success of this strategy, flavour responses which depend on taste - bitter, salt, sweet and sour - or trigeminal stimuli (e.g. creamy) were poorly predicted. As a result, caution should be exercised when the prediction of cheese flavour is based only on assessment of volatile components of the head space above the cheese. This caveat applies whether the characterisation is made by traditional head-space analysis, by a newly-developed 'electronic nose' or by a human assessor.