This paper presents an algorithm for online image-based terrain classification that mimics a human supervisor's segmentation and classification of training images into "Go" and "NoGo" regions. The algorithm identifies a set of image chips (or exemplars) in the training images that span the range of terrain appearance. It then uses the exemplars to segment novel images and assign a Go/NoGo classification. System parameters adapt to new inputs, providing a mechanism for learning. System performance is compared to that obtained via offline fuzzy c-means clustering and support vector machine classification.