We consider random variables which can be subject to both censoring and measurement errors. When considering different practical situations, two different models can be written to describe such situations in which the measurement errors affect only the variable of interest or also the censoring variable. Different estimation strategies can be proposed to estimate the density or hazard rate of the underlying variables of interest. We explain these models and strategies and provide L 2-risk bounds for the data driven resulting estimators. Simulations illustrate the performances of the estimators. Lastly, the method is applied to a real data set.