Recombination is a key evolutionary process that shapes the architecture of genomes and the genetic structure of populations. Although many statistical methods are available for the detection of recombination from DNA sequences, their absolute and relative performance is still unknown. Here we evaluated the performance of 14 different recombination detection algorithms. We used the coalescent with recombination to simulate DNA sequences with different levels of recombination, genetic diversity, and rate variation among sites. Recombination detection methods were applied to these data sets, and whether they detected or not recombination was recorded. Different recombination methods showed distinct performance depending on the amount of recombination, genetic diversity, and rate variation among sites. The model of nucleotide substitution under which the data were generated did not seem to have a significant effect. Most methods increase power with more sequence divergence. In general, recombination detection methods seem to capture the presence of recombination, but they are not very powerful. Methods that use substitution patterns or incompatibility among sites were more powerful than methods based on phylogenetic incongruence. Most methods do not seem to infer more false positives than expected by chance. Especially depending on the amount of diversity in the data, different methods could be used to attain maximum power while minimizing false positives. Results shown here will provide some guidance in the selection of the most appropriate method/s for the analysis of the particular data at hand.