Inversion techniques are the most powerful methods to obtain information about the thermodynamical and magnetic properties of solar and stellar atmospheres. In the last years, we have witnessed the development of highly sophisticated inversion codes that are now widely applied to spectro-polarimetric observations. However, no realistic confidence intervals are obtained for the inferred parameters. Additionally, it is fundamental to apply efficient techniques to assess the ability of models to reproduce the observations and to know to what extent the models have to be refined or can be simplified. We propose the application of Bayesian techniques. The posterior distribution, that takes into account both the a-priori knowledge and the information given by the data, is efficiently sampled using a Markov Chain Monte Carlo method. We demonstrate the capabilities of the code with the aid of an interesting example.