Interpretation of Human Immunodeficiency Virus 1 (HIV-1) genotypic drug resistance is still a major challenge in the follow-up of antiviral therapy in infected patients. Because of the high degree of HIV-1 natural variation, complex interactions and stochastic behaviour of evolution, the role of resistance mutations is in many cases not well understood. Using Bayesian network learning of HIV-1 sequence data from diverse subtypes (A, B, C, F and G), we could determine the specific role of many resistance mutations against the protease inhibitors (PIs) nelfinavir (NFV), indinavir (IDV), and saquinavir (SQV). Such networks visualize relationships between treatment, selection of resistance mutations and presence of polymorphisms in a graphical way. The analysis identified 30N, 88S, and 90M for nelfinavir, 90M for saquinavir, and 82A/T and 46I/L for indinavir as most probable major resistance mutations. Moreover we found striking similarities for the role of many mutations against all of these drugs. For example, for all three inhibitors, we found that the novel mutation 89I was minor and associated with mutations at positions 90 and 71. Bayesian network learning provides an autonomous method to gain insight in the role of resistance mutations and the influence of HIV-1 natural variation. We successfully applied the method to three protease inhibitors. The analysis shows differences with current knowledge especially concerning resistance development in several non-B subtypes.