HIV protease inhibitors are being used as frontline therapy in the treatment of HIV patients. Multi-drug-resistant HIV mutant strains are emerging with the initial aggressive multi-drug treatment of HIV patients. This necessitates continued search for novel inhibitors of viral replication. These protease inhibitors may further be useful as pharmacological agents for inhibition of other viral replication. Classification models of HIV Protease inhibitors are developed using a data set of 123 compounds containing several heterocycles. Their inhibitory concentrations expressed as log (IC50) ranged from -1.52 to 2.12 log units. The dataset was divided into active and inactive classes on the basis of their antiviral potency. Initially a two-class problem (active, inactive) is explored using k-nearest neighbor approach. In order to introduce non-linearity in the classifier different approaches were investigated. This led to the goal of a fast, simple, minimum user input, radial basis function neural network (RBFNN) classifier development. Then the same two-class problem was resolved using the (RBFNN) classifier. A genetic algorithm with RBFNN fitness evaluator was used to search for the optimum descriptor subsets. The application of majority rules was also tested for the RBFNN classification. The best six descriptor model found by the new cost function showed predictive ability in the high 80% range for an external prediction set.