Abstract: The accelerated failure time model provides a natural formulation of the effects of covariates on the failure time variable. The presence of censoring poses major challenges in the semi-parametric analysis. The existing semi-parametric estimators are computationally intractable. In this article we propose an unbiased transformation for the potential censored response variable, thus least square estimators of regression parameters can be gotten easily. The resulting estimators are consistent and asymptotically normal. Based on these, we can get a strongly consistent K-M estimator for the distribution of random error. Extensive simulation studies show that the asymptotic approximations are accurate in practical situations.