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Authenticity is an important food quality criterion. Rapid methods for confirming authenticity and detecting adulteration are widely demanded by food producers, processors, consumers and regulatory bodies. The objective of this work was to develop a model that would confirm the authenticity of Galician-labelled honeys as Galician-produced honeys. Nine metals were determined in 42 honey samples which were divided into two categories: Galician and non-Galician honeys. Multivariate chemometric techniques such as cluster analysis, principal component analysis, Bayesian methodology, partial least-squares regression and neural networks were applied to modelling classes on the basis of the chemical data. The results obtained indicated good performance in terms of classification and prediction for both the neural networks and partial least-squares approaches. The metal profiles provided sufficient information to enable classification rules to be developed for identifying honeys according to their geographical origin.