A fundamental problem with appearance-based recognition is how to encode the perceptual similarity between images as images need to be grouped based on their perceptual similarity. In this paper, we employ a spectral histogram model for generic appearance-based recognition. A perceptual component is defined as the spectral histogram of a training image, which encodes all the images perceptually similar to the input image. The similarity between two perceptual components is measured as χ distance between the corresponding spectral histograms, which has been shown to be perceptually meaningful. Building on this representation, we use the nearest neighbor classifier to classify an unseen input image, where each object class is represented by the perceptual components of the training images. A distinctive advantage of our representation is that it can be applied to many recognition problems, including texture classification, face recognition, and 3D object recognition.