This dissertation outlines the development of static and dynamic tomographic wave-front (WF) reconstructors tailored to Multi-Object Adaptive Optics (MOAO). They are applied to Raven, the first MOAO science and technology demonstrator recently installed on an 8m telescope, with the goal of increasing the limiting magnitude in order to increase sky coverage. The results of a new minimum mean-square error (MMSE) solution based on spatio-angular (SA) correlation functions are shown, which adopts a zonal representation of the wave-front and its associated signals. This solution is outlined for the static reconstructor and then extended for the use of standalone temporal prediction. Furthermore, it is implemented as the prediction model in a pupil plane based Linear Quadratic Gaussian (LQG) algorithm. The algorithms have been fully tested in the laboratory and compared to the results from Monte- Carlo simulations of the Raven system. The simulations indicate that an increase in limiting magnitude of up to one magnitude can be expected when prediction is implemented. Two or more magnitudes of improvement may be achievable when the LQG is used. These results are confirmed by laboratory measurements.