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Polyphenolic compositions of Basque and French ciders were determined by HPLC-DAD following thiolysis, in order to characterise and differentiate these beverages and then develop a classification system capable of confirming the authenticities of both kinds of cider. A data set consisting of 165 cider samples and 27 measured features was evaluated using multivariate chemometric techniques, such as cluster analysis and principal component analysis, in order to perform a preliminary study of data structure. Supervised pattern recognition techniques such as linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA), and multilayer feed-forward artificial neural networks (MLF-ANN) attained classification rules for the two categories using the chemical data, which produced satisfactory results. Authentication systems obtained by combining two of these techniques were proposed. We found that SIMCA and LDA or KNN models achieved 100% hit-rates, since LDA and KNN permit the detection of every Basque cider and SIMCA provides a model for Basque cider that excludes all French ciders. Polyphenolic profiles of the ciders provided enough information to be able to develop classification rules for identifying ciders according to their geographical origin (Basque or French regions). Chemical and organoleptic differences between these two types of cider are probably due to the original and distinctive cidermaking technologies used for their elaboration. Using polyphenic profiles, about 80% of French ciders could be distinguished according to their region of origin (Brittany or Normandy). Although their polyphenolic profiles did not provide enough information to achieve an authentication system for Breton and Norman ciders.