Categorical encoding is crucial for mastering large bodies of related sensory experiences. Recent single-unit recording studies in the macaque prefrontal cortex have demonstrated two characteristic forms of neural encoding of the sequential structure of the animal's behaviour. One population of neurons encodes the specific behavioural sequences. A second population of neurons encodes the sequence category (e.g. ABAB, AABB or AAAA) and does not differentiate sequences within the category [1]. Interestingly these neurons are intermingled in the lateral prefrontal cortex, and not topographically segregated. Here we report on a neural network simulation study that reproduces and explains these results. We simulate a cortical circuit as three 5x5 layers (infra-granular, granular, and supra-granular) of leaky integrator neurons with a sigmoidal output function, and we examine 103 such circuits running in parallel. The model is presented with 11 4-element sequences following Shima et al. We isolated one subpopulation of neurons each of whose activity predicts individual sequences, and a second population that predicts category independent of the specific sequence. We argue that a richly interconnected cortical circuit is capable of internally generating a neural representation of category membership, thus significantly extending the scope of recurrent network computation.