Object categorization is a crucial cognitive ability. It has also received much attention in machine vision. However, the computational processes underlying object categorization in cortex are still poorly understood. In this paper we compare data recorded by Freedman et al. from monkeys to that of view-tuned units in our HMAX model of object recognition in cortex [1],[2]. We FInd that the results support a model of object recognition in cortex [3] in which a population of shape-tuned neurons responding to individual exemplars provides a general basis for neurons tuned to different recognition tasks. Simulations further indicate that this strategy of first learning a general but object class-specific representation as input to a classifier simplifies the learning task. Indeed, the physiological data suggest that in the monkey brain, categorization is performed by PFC neurons performing a simple classification based on the thresholding of a linear sum of the inputs from examplar-tuned units. Such a strategy has various computational advantages, especially with respect to transfer across novel recognition tasks.