The incompleteness of data is a most common source of uncertainty in real-world Data Mining applications. The management of this uncertainty is, therefore, a task of paramount importance for the data analyst. Many methods have been developed for missing data imputation, but few of them approach the problem of imputation as part of a general data density estimation scheme. Amongst the latter, a method for imputing and visualizing multivariate missing data using Generative Topographic Mapping was recently presented. This model and some of its extensions are tested under different experimental conditions. Its performance is compared to that of other missing data imputation techniques, thus assessing its relative capabilities and limitations.