The scope of the classical k-NN classification techniques is enlarged under this study to cover partially exposed environments. The modified classification system structure required for successful operation in environments, wherein all the inherent pattern classes are not exposed to the system prior to deployment, is developed and illustrated with the aid of a specific classification rule-the neighborhood census rule (NCR). Admittedly, alternative rules can be visualized to fit this modified structure. However, this study concentrates on the use of NCR to bring out the underlying philosophy and develops optimum thresholds for admittance of unknown samples into the set of presently known classes. These thresholds are learned from the available training samples of these classes. This learning represents a new dimensionality of the learning system structure in that estimates of the domains of the known classes are developed in addition to learning of the discrimination among these classes. This facilitates identification of samples belonging to the classes previously unexposed to the recognition system. Experimental results are also presented in support of the proposed concepts and methodology for operation in partially exposed environments.