We work towards efficient methods of categorizing visual content in medical images as a precursor step to segmentation and anatomy recognition. In this paper, we address the problem of automatic detection of level/position for a given cardiac CT slice. Specifically, we divide the body area depicted in chest CT into nine semantic categories each representing an area most relevant to the study of a disease and/or key anatomic cardiovascular feature. Using a set of handcrafted image features together with features derived form a deep convolutional neural network (CNN), we build a classification scheme to map a given CT slice to the relevant level. Each feature group is used to train a separate support vector machine classifier. The resulting labels are then combined in a linear model, also learned from training data. We report margin zero and margin one accuracy of 91.7% and 98.8% and show that this hybrid approach is a very effective methodology for assigning a given CT image to a relatively narrow anatomic window.