We present new experimental and mathematical techniques aimed at determining the features used in visual object recognition. We conceive of these features as the parts of an object that are treated as unitary wholes when recognizing or discriminating visual objects. For example, consider a task classifying a visual target presented in pixel noise as a “P” or a “Q”. The features may correspond to particular shapes of the target letters. Two such features for “P”, for example, might be a vertical line and upper-right-facing curve. The decision may be encoded in terms of particular values of such features, and an appropriate combination of these values may determine how the expression is perceived. We utilize recent advances in statistical machine learning techniques to uncover the features used by human observers.