Adaptive regulation of food consumption involves both identifying food as well as evaluating whether it should be eaten, a process that requires assessing relevant properties such as healthfulness and hedonic value. In order to identify how these fine-grained food properties are represented in the brain, we analyzed functional Magnetic Resonance Imaging data from 43 participants who viewed images of 36 different foods. A data-driven clustering approach based on Representational Similarity Analysis partitioned food-responsive brain regions into two sub-networks based on their multivariate response to food pictures: a Prefrontal network composed of fronto-parietal brain regions and a Limbic network composed of cortico-limbic and sub-cortical brain regions. Further analysis, using similarity judgments of those foods from a large online sample, revealed that the Prefrontal network predominantly represented information related to food healthfulness or processing, the key factor underlying food similarity. In another imaging task, we found that responses in the Prefrontal network were strongly influenced by judgments of food-related self-control, while the Limbic network responses were more affected by hedonic food judgments. These results suggest that, upon viewing food images, behaviorally relevant information is automatically retrieved from distinct brain networks that act as opponent processes in guiding food consumption.