Visual search requires humans to detect a great variety of target objects in scenes cluttered by other objects or the natural environment. It is unknown whether the brain contains a general purpose detection mechanism to code the presence of a wide variety of categories of objects embedded in natural scenes. We provide evidence for a feature-independent coding mechanism for detecting behaviorally relevant targets in natural scenes in the dorsal frontoparietal network. Pattern classifier using data from single-trial fMRI activity in the dorsal frontoparietal network reliably predicted the presence of 384 different target objects and also the observer's choices. IPS activity correlated with observers' decision confidence and with the task-difficulty of individual images. These results could not be explained based on physical differences across images or eye-movements. Thus, the dorsal frontoparietal network detects behaviorally relevant targets in natural scenes independent of the defining visual features and is akin to the priority map in monkey lateral intra-parietal cortex.