Visual crowding, the impairment of object recognition in peripheral vision due to flanking objects, has generally been studied using simple stimuli on blank backgrounds. While crowding is widely assumed to occur in natural scenes, it has not been shown rigorously yet. Given that scene contexts can facilitate object recognition, crowding effects may be dampened in real-world scenes. Therefore, this study investigated crowding using objects in computer-generated real-world scenes. In two experiments, target objects were presented with four flanker objects placed uniformly around the target. Previous research indicates that crowding occurs when the distance between the target and flanker is approximately less than half the retinal eccentricity of the target. In each image, the spacing between the target and flanker objects was varied considerably above or below the standard (0.5) threshold to either suppress or facilitate the crowding effect. Experiment 1 cued the target location and then briefly flashed the scene image before participants could move their eyes. Participants then selected the target object’s category from a 15-alternative forced choice response set (including all objects shown in the scene). Experiment 2 used eye tracking to ensure participants were centrally fixating at the beginning of each trial and showed the image for the duration of the participant’s fixation. Both experiments found object recognition accuracy decreased with smaller spacing between targets and flanker objects. Thus, this study rigorously shows crowding of objects in semantically consistent real-world scenes.