5. A pattern recognition process for generating a confidence level that an unknown object which belongs to one of a plurality of models actually belongs to a selected model, the pattern recognition process for use with a constraint network, the constraint network providing a network of constraints representing one of the plurality of models, the constraint network comprising a plurality of weights, a plurality of nodes and a plurality of layers wherein each layer of nodes corresponds to a particular feature and each node is associated with a sub-range of the typical values for that feature for a selected one of the plurality of models, and wherein each weight corresponds to a value representing a relationship between the nodes of at least one feature type and the selected model, the pattern recognition process comprising the steps of: (a) receiving feature values of an unknown object, wherein each feature value has a known range; and (b) responsive to the relationship between those nodes that correspond to the feature values and the weights between those nodes, generating a value corresponding to the confidence that the object belongs to the selected model.
5. A pattern recognition process for generating a confidence level that an unknown object which belongs to one of a plurality of models actually belongs to a selected model, the pattern recognition process for use with a constraint network, the constraint network providing a network of constraints representing one of the plurality of models, the constraint network comprising a plurality of weights, a plurality of nodes and a plurality of layers wherein each layer of nodes corresponds to a particular feature and each node is associated with a sub-range of the typical values for that feature for a selected one of the plurality of models, and wherein each weight corresponds to a value representing a relationship between the nodes of at least one feature type and the selected model, the pattern recognition process comprising the steps of: (a) receiving feature values of an unknown object, wherein each feature value has a known range; and (b) responsive to the relationship between those nodes that correspond to the feature values and the weights between those nodes, generating a value corresponding to the confidence that the object belongs to the selected model. 8. The pattern recognition process of claim 5, wherein each node corresponds to an ordered sub-range of values.