The increased availability of large amount of administrative information at the Italian Institute of Statistics (Istat) makes it necessary to investigate new methodological approaches for the production of estimates, based on combining administrative data with statistical survey data. Traditionally, administrative data have been used as auxiliary sources of information in different phases of the production process such as sampling, calibration, imputation. In order to take into account deficiencies in the measurement process of both survey and administrative sources, a more symmetric approach with respect to the available sources can be adopted. A natural strategy, according to this approach, is to consider the target variables as latent (unobserved) variables, and to model the measurement processes through the distributions of the observed variables conditional on the latent variables. In this context, Latent Class Analysis (LCA) is traditionally considered as a method to identify a categorical latent variable using categorical observed variables, which a longitudinal extension is the Hidden Markov Model (HMM). Examples on the use of latent models in Official Statistics are becoming common and several applications can be found in the field of employments research. In this paper we show the use of a latent model for estimating employment rates in Italy using both Labour Force Survey(LFS) and administrative data. Here, the use of HMM is particularly suitable since, as for many (European) countries, employment administrative data are collected on a monthly bases, while the LFS data contains a rotating panel structure The aim is both to show which are the potentiality of this methodology and the possible problems and research topics.