The contribution of volatile aroma compounds to the overall composition and sensory perception of wine is well recognized. The classical targeted measurement of volatile compounds in wine using GC-MS is laborious and only a limited number of compounds can be quantified at any time. Application of an automated multivariate curve resolution technique to nontargeted GC-MS analysis of wine makes it possible to detect several hundred compounds within a single analytical run. Hunter Valley Semillon (HVS) is recognized as a world class wine with a range of styles. Subtle characters reliant upon the development of bottle maturation characteristics are a feature of highly esteemed HVS. In this investigation a metabolomic approach to wine analysis, using multivariate curve resolution techniques applied to GC-MS profiles coupled with full descriptive sensory analysis, was used to determine the objective composition of various styles of HVS. Over 250 GC-MS peaks were extracted from the wine profiles. Sensory scores were analyzed using PARAFAC prior to development of predictive models of sensory features from the extracted GC-MS peak table using PLS regression. Good predictive models of the sensorial attributes honey, toast, orange marmalade, and sweetness, the defining traits for HVS, could be determined from the extracted peak tables. Compound identification for these rated attributes indicated the importance of a range of ethyl esters, aliphatic alcohols and acids, ketones, aldehydes, furanic derivatives, and norisoprenoids in the development of HVS and styles. The development of automated metabolomic data analysis of GC-MS profiles of wines will assist in the development of wine styles for specific consumer segments and enhance understanding of production processes on the ultimate sensory profiles of the product.