The analysis of digitized images from polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP)gel electrophoresis examinations is a popular method for virus typing, i.e., for identifying the virus type(s) that have infected an investigated biological sample. However, being mostly manual, the conventional virus typing protocol remains laborious, time consuming, and error prone. In order to overcome these shortcomings,we propose a computerized methodology for improving virus typing via PCR-RFLP gel electrophoresis. A novel realistic observation model of the viral DNA motion on the gel matrix is employed to assist in exploiting additional virus-related information in comparison to the conventional approaches. The extracted rich information is fed to a novel typing algorithm, resulting in faster and more accurate decisions. The proposed methodology is evaluated for the case of the human papillomavirus typing on a dataset of 80 real and 1500 simulated samples, producing very satisfactory results.Ind