Recently, Dynamic Neural Field models have shed light on the flexible and dynamic processes underlying young children’s emergent categorisation and word learning (DNF; e.g., Spencer & Schöner [1]). DNF models are a distinct class of neural network in which perceptual features can be represented topologically and time continuously, complementing existing connectionist models of cognitive development by building category representations that are available for inspection at any given stage in learning. Recent research in infant categorization and word learning has demonstrated that young children’s ability to learn and generalise labels for novel object categories is profoundly affected by the perceptual variability of the to-be-learned category. We have captured these data in a DNF model of children’s category label learning. Given a known vocabulary, our model exploits mutual exclusivity via simple associative processes to map novel labels to novel categories, and is able to retain and generalize these newly-formed mappings. The model was used to generate the testable prediction that children’s generalizations of novel category labels should be contingent on the number and closeness of objects’ perceptual neighbours. We present a replication of this prediction, via an empirical study with 30-month-old children. In line with the model, children were only able to generalize novel words to completely novel objects when those objects were central to the just-encountered category, rather than peripheral. This empirical replication demonstrates the predictive validity of DNF models when applied to cognitive development. Further, the data suggest that children’s ability to categorise and learn labels is not a conceptually-based, stepwise phenomenon, but rather a graded, emergent process. As such, these data add weight to associative, dynamic systems approaches to understanding language learning, categorisation, and cognition more generally.