The use of visible (vis) and near-infrared spectroscopy (NIR) was explored as a tool to discriminate between samples of Australian commercial white wines of different varietal origins (Chardonnay and Riesling). Discriminant models were developed using principal component analysis (PCA), principal component regression (PCR), and discriminant partial least-squares (DPLS) regression. The samples were randomly split into two sets, one used as a calibration set (n = 136) and the remaining samples as a validation set (n = 133). When used to predict the variety of the validation set samples, the DPLS models correctly classified 100% of Riesling and up to 96% of Chardonnay wines. These results showed that vis-NIR might be a suitable and alternative technology that can be easily implemented by the wine industry to discriminate Riesling and Chardonnay commercial wine varieties. However, the relatively limited number of samples and varieties involved in the present work suggests caution in extending the potential of such a technique to other wine varieties.