In this article, we consider nonparametric regression when covariates are measured with error. Estimation is performed using boosted regression trees, with the sum of the trees forming the estimate of the conditional expectation of the response. Both binary and continuous response regression are investigated. An approach to fitting regression trees when covariates are measured with error is described, and the boosting algorithms consist of its repeated application. The main feature of the approach is that it handles situations where multiple covariates are measured with error. Some simulation results are given as well as its application to data from the Framingham Heart Study.