In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about one-hundred-and-fifty real raw range images of object instances from ten classes.