We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan–Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method. Copyright © 2012 John Wiley & Sons, Ltd.