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Frequent and spatially distributed measurements of soil moisture (SMC), at different spatial scales, are advisable for all applications related to the environmental disciplines, such as climatology, meteorology, hydrology and agriculture. Satellite sensors operating in the low part of microwave spectrum represent an important tool for this purpose, and their signals can be directly related to the moisture content of the observed surfaces, provided that all the contributions from soil and vegetation to the measured signal are properly accounted for. Among the algorithms used for the retrieval of SMC from microwave sensors (both active, such as Synthetic Aperture Radar-SAR, and passive, radiometers), the artificial neural networks (ANN) represent the best compromise between accuracy and computation speed. ANN based algorithms have been developed at IFAC, and adapted to several radar and radiometric satellite sensors, in order to generate SMC products at a resolution varying from hundreds of meters to tens of kilometers (according to the spatial scale of each sensor ). These algorithms, which are based on the ANN techniques for inverting theoretical and semi-empirical models, such as Advanced Integral Equation (AIEM) and Oh models and on Radiative transfer Theory (RTT, in the simplified form of the 'Water-Cloud' model), have been adapted to the C- to Ka- band acquisitions from spaceborne radiometers (AMSR-E/AMSR2), SAR (Envisat/ASAR, Cosmo-SkyMed) and real aperture radar (MetOP ASCAT). Large datasets of co-located satellite acquisitions and direct SMC measurements on several test sites worldwide have been used along with simulations derived from forward electromagnetic models for setting up, training and validating these algorithms. An overall quality assessment of the obtained results in terms of accuracy and computational cost was carried out, and the main advantages and li