This paper describes a new technique for modeling 3D objects that is applicable to recog-nition tasks in advanced automation. Objects are represented in terms of canonic 2D models which can be used to determine the identity, location and orientation of an unknown object. The reduction in dimensionality is achieved by factoring the space of all possible perspective projections of an object into a set of characteristic views, where each such view defines a characteristic-view domain within which all projections are topologically identical and related by a linear transformation. The characteristic views of an object can then be hierarchically structured for efficient classification. The line-junction labelling constraints are used to match a characteristic view to a given unknown-object projection, and determination of the unknown-object projection-to-characteristic view transformation then provides information about the identity as well as the location and orientation of the object.