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Recombination is a fundamental evolutionary force, but it is difficult toquantify because the effect of a recombination event on patterns of variationin a sample of genetic data can be hard to discern. Estimators for therecombination rate, which are usually based on the idea of integrating over theunobserved possible evolutionary histories of a sample, can therefore be noisy.Here we consider a related question: how would an estimator behave if theevolutionary history actually was observed? This would offer an upper bound onthe performance of estimators used in practice. In this paper we derive anexpression for the maximum likelihood estimator for the recombination ratebased on a continuously observed, multi-locus, Wright--Fisher diffusion ofhaplotype frequencies, complementing existing work for an estimator ofselection. We show that, contrary to selection, the estimator has unusualproperties because the observed information matrix can explode in finite timewhereupon the recombination parameter is learned without error. We also showthat the recombination estimator is robust to the presence of selection in thesense that incorporating selection into the model leaves the estimatorunchanged. We study the properties of the estimator by simulation and show thatits distribution can be quite sensitive to the underlying mutation rates.

An estimator for the recombination rate from a continuously observed diffusion of haplotype frequencies