In late 1988, Miyashita published work reporting recordings of single cells in the inferotemporal cortex of the macaque monkey (Miyashita 1988 Nature 335 817-20). He described the responses of neurons to a sequence of random fractal pattern images, and how many of the neurons tested were seen to respond strongly to a subset of the images on the basis of sequence presentation order, i.e. appearance in time, rather than their spatial similarity. In this work, I describe a local, Hebb-like learning rule which in conjunction with a simple feedforward neural architecture is capable of replicating the type of temporal-order association apparent in the cells from which he made recordings. The paper also advances reasons for requiring such learning by describing its possible role in establishing transformation invariant representations of objects.