Tolerance intervals are useful tools to capture characteristics of the un- derlying distribution of collected data in industrial, clinical trials and phar- maceutical applications. In real applications, especially in reliability test- ing and clinical trial, it is common that the collected data with censored outcomes. Although there are existing methods for constructing tolerance interval for specic distributions or models, there lacks a unied approach for constructing tolerance intervals with censored data for any distribution. In this study, we consider the problem of constructing tolerance intervals for parametric distributions with censored data. A censored rate approach is proposed to estimate the parameters. Algorithms based on the estimation to construct tolerance intervals for the normal and other distributions are provided in this study. A simulation study and a real data example study show the superiority of the proposed methods.