The verification of the geographical origin of olive oils by analytical techniques is still a challenge. The goal of this work is to explore the application and accuracy of different chemometric tools combined with near infrared spectroscopy (NIR) based analytical methods in the field of geographical authenticity of olive oils. As olive oils associated with different geographical origins are mainly characterized by different fatty acid (FA) and triacylglycerol (TAG) compositions, NIR methods for the fast and reliable determination of these parameters are developed. Next, these NIR methods are used to characterize a comprehensive set of olive oils (n > 5000) derived from 19 different countries. This set of data is used to build a statistical workflow, which allows the determination of the geographical origin of unknown olive oil samples. First of all, the untreated data set is pretreated by k‐means clustering and the selection of the relevant analytical variables by principal component analysis (PCA) and linear discriminant analysis (LDA) and min/max normalization of all parameters. Subsequently, classification is performed with a reduced sample set of the 200 most similar samples identified by k‐nearest neighbor tool (kNN). For classification purpose kNN, LDA, naïve Bayes classifier, and logit regression are applied. Practical Applications: The established statistical workflow can be used to verify the geographical origin of olive oils. The application and usage of up to four different statistical models for classification purpose results in a superior probability of the predicted origin in comparison to the application of only one single statistical classification test. As standardized methods are used as reference methods for building the NIR methods, the FA and TAG composition and the iodine value can be either determined by the standard methods or by the described NIR method. The presented statistical approach will help to build up a system for the verification of the geographical origin of olive oils.