Statistical problems in modelling personal income distributions include estimation procedures, testing, and model choice. Typically, the parameters of a given model are estimated by classical procedures such as maximum likelihood and leastsquares estimators. Unfortunately, the classical methods are very sensitive to model deviations such as gross errors in the data, grouping effects or model misspecifications. These deviations can ruin the values of the estimators and inequality measures and can produce false information about the distribution of the personal income in a given country. In this paper we discuss the use of robust techniques for the estimation of income distributions. These methods behave as the classical procedures at the model but are less influenced by model deviations and can be applied to general estimation problems.