The Raven’s Progressive Matrices intelligence test is widely used as a measure of Spearman’s general intelligence factor g. Although Raven’s problems resemble geometric analogies, prior computational accounts of solving the test have been propositional. Studies of both typical and atypical human behavior suggest the possible existence of visual strategies; for example, neuroimaging data indicates that individuals with autism may preferentially recruit visual processing brain regions when solving the test. We present two different algorithms that use visual representations to solve Raven’s problems. These algorithms yield performances on the Standard Progressive Matrices test at levels equivalent to typically developing 9.5- and 10.5- year-olds. We find that these algorithms perform most strongly on problems identified from factor-analytic human studies as requiring gestalt or visuospatial operations, and less so on problems requiring verbal reasoning. We discuss implications of this work for understanding the computational nature of Raven’s and visual analogy in problem solving.