Ecological communities are organized in complex ecological networks. Trait-based analyses of the structure of these networks in highly diversified species assemblages are crucial for improving our understanding of the ecological and evolutionary processes causing specialization in mutualistic networks. In this study, we assessed the importance of morphological traits for structuring plant–hummingbird networks in Neotropical forests by using a novel combination of quantitative analytical approaches. We recorded the visitation of hummingbirds to plant species over an entire year at three different elevations in Costa Rica and constructed quantitative networks based on interaction frequencies. Three morphological traits were measured in hummingbirds (bill length, bill curvature, and body mass) and plants (corolla length, curvature, and volume). We tested the effects of avian morphological traits and abundance on ecological specialization of hummingbird species. All three morphological traits of hummingbirds were positively associated with ecological specialization, especially bill curvature. We tested whether interaction strength in the networks was associated with the degree of trait matching between corresponding pairs of morphological traits in plant and hummingbird species and explore whether this was related to resource handling times by hummingbird species. We found strong and significant associations between interaction strength and the degree of trait matching. Moreover, the degree of trait matching, particularly between bill and corolla length, was associated with the handling time of nectar resources by hummingbirds. Our findings show that bill morphology structures tropical plant–hummingbird networks and patterns of interactions are closely associated with morphological matches between plant and bird species and the efficiency of hummingbirds' resource use. These results are consistent with the findings of seminal studies in plant–hummingbird systems from the neotropics. We conclude that trait-based analyses of quantitative networks contribute to a better mechanistic understanding of the causes of specialization in ecological networks and could be valuable for studying processes of complementary trait evolution in highly diversified species assemblages.