Abstract Various aspects of semantic features associated with concepts drive early vocabulary development, but less is known how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distance between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word’s global feature distinctiveness is measured with respect to all other words in the network and a word’s local feature distinctiveness is measured relative to words in sub-networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development that occurs over the lifespan, beyond that of early childhood. Keywords: semantic features, global distinctiveness, local distinctiveness, language acquisition, Manhattan distance, network analysis Manuscript under review