Embodied theories of language posit that the human brain’s adaptations for language exploit pre-existing perceptual and motor mechanisms for interacting with the world. In this paper we propose an embodied account of the linguistic distinction between singular and plural, encoded in the system of grammatical number in many of the world’s languages. We introduce a neural network model of visual object classification and spatial attention, informed by a collection of findings in psychology and neuroscience. The classification component of the model computes the type associated with a visual stimulus without identifying the number of objects present. The distinction between singular and plural is made by a separate mechanism in the attentional system, which directs the classifier towards the local or global features of the stimulus. The classifier can directly deliver the semantics of uninflected concrete noun stems, while the attentional mechanism can directly deliver the semantics of singular and plural number features.