In a global food market, Protected Designation of Origin (PDO) wines are highly appreciated for the period of aging that allows the evolution of unique organoleptic properties, and thus, the guarantee of the vintage age is a critical authenticity issue [1]. The coupling of SPME-Arrow to GCxGC/MS analysis in combination with chemometric techniques could revolutionize the field of wine authenticity with the establishment of characteristic volatile markers according to the vintage age. In this work, an SPME-Arrow-GCxGC/MS method was optimized and used for the determination of volatile markers in 24 monovarietal red wine samples belonging to the PDO Xinomavro Naoussa, produced during 4 different years (1998, 2005, 2008 and 2015), in Northern Greece. Overall, 258 volatile compounds were tentatively identified. The data matrix was further processed with multivariate techniques to establish mathematical models and reveal volatile markers for each vintage age. A partial least square – discriminant analysis (PLS-DA) model was developed and successfully classified all the samples to the proper class according to the vintage age with an explained total variance of 95.7%. Variant Importance in Projection (VIP) algorithm was used to calculate the VIP scores of the determined volatiles and distinguish the most important features that affect the discrimination, revealing markers [2]. The developed prediction model was validated and the analyzed samples were classified with 100% accuracy according to the vintage age, on the basis of their volatile fingerprint.