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Recently, food authenticity has raised worldwide attention in food manufacturing and a growing concern about food qualification, based on a clear regional identity, is noticed. Therefore, the development of suitable methodologies allowing the characterization of different products, based on their geographical origin, is of great importance. In this study, the potential of gas chromatographic fatty acid fingerprints in combination with multivariate data analysis was examined to classify walnuts from different regions in Iran according to their geographical origins. Walnut samples were collected during the harvesting period 2013–2014 from six regions in Iran. Chromatographic fingerprints of the walnut oil were employed to discriminate the walnut origin. Principal component analysis-Linear discriminant analysis (PCA-LDA) results showed that the six regions of geographical origin can be identified based on the fatty acid fingerprints. Almost all samples were correctly classified by the PCA-LDA model using cross validation (99.2%). The average percent correct classification for the prediction set was 98.3%, indicating the satisfactory performance of the model. A high percentage of correct classifications for the training data demonstrates the strong relationship between the fatty acid profile and the origin, while a high percentage for the prediction set shows the ability to indicate the origin of an unknown sample based on its fatty acid chromatographic data. [ABSTRACT FROM AUTHOR]