The visual word form area (VWFA) is a region of the cortex located in the left fusiform gyrus, that appears to be a waystation in the reading pathway, but there is a disagreement as to whether or not the VWFA is selective for whole words or sublexical structures. A recent study using fMRI adaptation (Glezer, et al., 2009) provided evidence that neurons in the VWFA are selectively tuned to real words, but not pseudowords, suggesting the VWFA is tuned to real words and not sublexical structure. Here, we develop a realistic model of the VWFA by training a deep convolutional neural network to map printed words to their labels. The network is able to achieve an accuracy of 98.5% on the test set. We then analyze this network to see if it can account for the data Glezer et al. found for words and pseudowords, and find that it does.