In (his work, the combination of hydrogen nuclear magnetic resonance (H-1 NMR) fingerprinting of gasoline with pattern-recognition analyses provides an approach to distinguish Brazilian commercial gasoline, processed in different states of Brazil. Hierarchical cluster analyses (HCA) and principal component analyses (PCA) were carried out on chemical shifts in order to observe any natural grouping feature. while soft independent modeling of class analogy (SIMCA) was performed to classify external samples into previously origin-defined classes. PCA demonstrated that a small number of variables dominate the total data variability since the first three principal components (PCs) accounted for 64.9% of total variability; whereas a HCA dendrogram shows five natural cluster grouping features. Following optimized H-1 NMR-SIMCA algorithm, sensitivity values in the training set with leave-one-out cross-validation (86.0%) and external prediction set (77.3%) were obtained. Governmental laboratories could employ this method as a rapid screening analysis for origin authentication related to tax evasion purposes.