This project develops a framework called Cresceptron for automatic learning for recognition and segmentation of real-world objects from their images based on exemplars of performance of such tasks. The Cresceptron has been tested on the task of visual recognition: recognizing 3-D general objects from 2-D electro-optical images of natural scenes and segmenting the recognized objects from their cluttered image background. Specifically, it recognizes and segments image patterns that are similar to those learned, using a stochastic distortion model and view-based interpolation, allowing other view points that are moderately different from those used in learning. It incorporates both individual learning and class learning; with the former, each training example is treated as a different individual and with the later, each example is a sample of a class. Several types of network structures have been developed, and their properties are addressed in terms of knowledge recallability, positional invariance, generalization power, discrimination power and space complexity. Experiments with a variety of real-world images are reported to demonstrate the feasibility of the Cresceptron.