In the real world, Al systems arc constantly and adversely influenced bynoisy data. This is also true of EBL (explanation-based learning). This paper discusseshow to cope with noisy data in explanation - based learning and proposes a NR -EBL(noise resistant explanation - based learning) algorithm. Unlike existing algorithms,NR-EBI. can learn macro rules and find the problem distribution when there is noise intraining examples. Also unlike similar work, NR-EBI. reveals the dependency of classifying examples correctly upon the regularities of noise and attempts to detect noise regularities from a set of training examples. With the help of knowledge of noise regularities,NR -EBI. can have a higher rate of correct recognition than traditional algorithms and previous work. NR EBI. is the combination of explanation - based learning and statisticalpattern recognition. Traditional algorithms are only special cases of NR-EBL when thereis no noise in training examples.