The chemometric philosophy is based on some well-defined and shared principles: multivariate analysis of the problem, searching for the relevant information of data, model validation to produce predictive models, definition of the model applicability domain for reliable predictions and graphical visualization of the relevant information. The verification of food authenticity is fundamentally a classification problem; indeed, samples are labeled according to their authenticity status, that is, authentic or non-authentic. Chemometric methods are usually applied to data that are collected in a numerical table. Chemometric methods usually require a pre-treatment of the raw data in order to be able to extract useful and non-trivial information and discard obvious (i.e., intrinsic in the single variable) and useless information. Soft independent modeling of class analogy is one of the most popular class modeling approaches. Artificial Neural Networks are currently considered among the most important emerging tools in chemometrics.