A key challenge of object recognition is achieving a balance between selectivity for relevant features and invariance to irrelevant ones. Computational and cognitive models predict that optimal selectivity for features will differ by object, and here we investigate whether this is reflected in visual representations in the human ventral stream. We describe a new real-time neuroimaging method, dynamically adaptive imaging (DAI), that enabled measurement of neural selectivity along multiple feature dimensions in the neighborhood of single referent objects. The neural response evoked by a referent was compared to that evoked by 91 naturalistic objects using multi-voxel pattern analysis. Iteratively, the objects evoking the most similar responses were selected and presented again, to converge upon a subset that characterizes the referent's "neural neighborhood." This was used to derive the feature selectivity of the response. For three different referents, we found strikingly different selectivity, both in individual features and in the balance of tuning to sensory versus semantic features. Additional analyses placed a lower bound on the number of distinct activation patterns present. The results suggest that either the degree of specificity available for object representation in the ventral stream varies by class, or that different objects evoke different processing strategies.