pHu and palatability traits (tenderness, juiciness and beef flavor intensity) of 480 young bulls were investigated. For each trait, quality categories were built using unsupervised learning methods to relate them with the farm-to-table continuum data. The categorization into high, medium and low-quality clusters showed differences at the whole levels of the continuum including animal and rearing factors, carcass, muscle and meat parameters. Isocitrate dehydrogenase activity, weight at the beginning of the fattening period and fattening duration were robust discriminators within the clusters. When focused on palatability traits, the different estimates of fat (%fat carcass, fat carcass weight of the 6th rib, fatness score and intramuscular fat) and %muscle carcass allow the categorization of the traits into clusters. Furthermore, the tender, juicy and flavorful meat had the lowest total collagen content as the corresponding animals fed with the lowest %forage and dry matter intake. The findings evidenced the potential of the information from the different levels of the farm-to-table continuum to achieve the desirable beef quality.