The study presents a multi-modal parcellation of the human cerebral cortex, aiming to map its major subdivisions (cortical areas) with high precision. Using data from the Human Connectome Project (HCP), researchers developed an objective semi-automated neuroanatomical approach to delineate 180 areas per hemisphere, characterized by sharp changes in cortical architecture, function, connectivity, and topography. The parcellation was validated through cross-subject analysis, demonstrating high reproducibility and the ability to detect atypical areal patterns in some individuals. A machine-learning classifier was trained to recognize the multi-modal 'fingerprint' of each cortical area, achieving 96.6% detection rate in new subjects and replicating the group parcellation. The freely available parcellation and classifier will enhance neuroanatomical precision in studies of the structural and functional organization of the human cerebral cortex across individuals, development, aging, and disease. The approach combines architectural, functional, connectivity, and topographic properties, using advanced image preprocessing and registration techniques to improve alignment and reduce variability. The study also discusses the implications for clinical applications and the understanding of human cortical evolution.The study presents a multi-modal parcellation of the human cerebral cortex, aiming to map its major subdivisions (cortical areas) with high precision. Using data from the Human Connectome Project (HCP), researchers developed an objective semi-automated neuroanatomical approach to delineate 180 areas per hemisphere, characterized by sharp changes in cortical architecture, function, connectivity, and topography. The parcellation was validated through cross-subject analysis, demonstrating high reproducibility and the ability to detect atypical areal patterns in some individuals. A machine-learning classifier was trained to recognize the multi-modal 'fingerprint' of each cortical area, achieving 96.6% detection rate in new subjects and replicating the group parcellation. The freely available parcellation and classifier will enhance neuroanatomical precision in studies of the structural and functional organization of the human cerebral cortex across individuals, development, aging, and disease. The approach combines architectural, functional, connectivity, and topographic properties, using advanced image preprocessing and registration techniques to improve alignment and reduce variability. The study also discusses the implications for clinical applications and the understanding of human cortical evolution.