In this paper, we compare two imputation procedures based on Bayesian networks. One method imputes missing items of a variable taking advantage only on information of its parents, while the other takes advantage of its Markov blanket. The structure of the paper is as follows. The first section contains an illustration of Bayesian networks. Then, we explain how to use the information contained in Bayesian networks in Section 2. In Section 3, we describe two evaluation indicators of imputation procedures. Finally, a Monte Carlo evaluation is carried on a real data set in Section 4.