<p>Crude oil is a complex mixture of various chemical components, and each crude oil may behave differently during production, transportation, storage, and refining. The typical approach to treating crude oils produced from different wells or oil fields is to classify them into distinct groups and treat each group similarly. However, traditional methods of crude oil classification focus on particular characteristics and ignore others. Hence, a multidisciplinary approach is necessary to benefit both upstream and downstream industries. Employing the structural characteristics of asphaltenes to classify crude oils is the most effective solution. This study introduces an innovative chemometric method for the first time, integrating the Modified Moving Window Correlation Coefficient (MMWCC), Multidimensional Scaling (MDS), and Hierarchical Cluster Analysis (HCA) techniques. This approach enabled a robust and comprehensive classification of 93 crude oil samples from the Asmari reservoir, spanning 21 different oil fields in the Dezful Embayment, Southwest Iran. By analyzing the FTIR spectra of asphaltenes, a dissimilarity matrix was constructed using the MMWCC method. Subsequently, MDS was employed to visualize these dissimilarities in a 2D and 3D space. Finally, HCA was utilized to cluster the samples into distinct groups based on Euclidean distance in the 3D space. This approach optimizes the clustering threshold by considering the spatial proximity of samples. The proposed method successfully classified the Asmari reservoir crude oils into nine distinct groups, providing insights into their asphaltene origins. Complementary analyses, including XRD and ICP-OES, further validated the defined oil groups. This novel approach offers an efficient, straightforward, cost-effective, and multidisciplinary solution for crude oil classification, providing valuable information for both upstream and downstream industries.</p>