Despite its sequence variability and structural flexibility, the V3 loop of the HIV-1 envelope glycoprotein gp120 is capable of recognizing cell-bound co-receptors CCR5 and CXCR4 and infecting cells. Viral selection of CCR5 is associated with the early stages of infection, and transition to selection of CXCR4 indicates disease progression. We have developed a predictive statistical model for co-receptor selectivity that uses the discrete property of net charge and the binary co-receptor preference markers of the NX[T/S]X glycosylation motif and 11/24/25 positive amino acid rule. The model is based on analysis of 2,054 V3 loop sequences from patient data and allows us to infer the most likely state of the disease from physicochemical characteristics of the sequences. The performance of the model is comparable to established sequence-based predictive methods, and may be used in combination with other methods as a supportive diagnostic for co-receptor selection. This model may be used for personalized medical decisions in administering co-receptor-specific therapies.