In an earlier work on the design of fine-grain, scalable classifiers for massively parallel computers, the technique of unifying cascaded networks has been demonstrated. This paper further examines the method adopted using a highly parallel processing architecture, entitled Unified Hierarchical Classifiers (UHC), based on the principles of Generalized Regression Neural Networks (GRNN). As with the GRNN, it has been shown that the resulting classification network can be implemented efficiently on general-purpose multiprocessor platforms without dedicated hardware for processor interconnections. Adding to this the structural simplicity, and the demonstrable potential for an effective distributed realization on the Cray T3D, will make UHC an attractive classifier architecture in practical applications.