The increasing number of oceanic observations calls for the use of synthetic methods to provide consistent analyses of the oceanic variability that will support a better understanding of the underlying mechanisms. In this study, a 1/3 degrees eddy-permitting model of the North Atlantic (from 20 degrees S to 70 degrees N) is combined with a 4D-variational method to estimate the oceanic state from altimeter observations. This resolution allows a better extraction of the physical content of altimeter data since the model spatial scales are more consistent with the data than coarser assimilation exercises because of a lower error in model representativity. Several strategies for the assimilation window are tested through twin experiments carried out under the following conditions: different window lengths and either a quasi-static (also known as progressive) variational assimilation with progressive extension of the window, or a simpler direct method without prior assimilation. From our set of experiments, the most efficient strategy is the use of both a simple direct assimilation method and a 90-day window. The assimilation of synthetic altimeter data constrains the model-temperature, -salinity and -velocity fields mainly over the first 1300 m where the error is the largest. Improvements occur not only in quiescent regions, but also in more energetic meso-scale regimes. Despite the existence of model- and surface forcing-errors as well as large errors in the first guess, the assimilation of real altimeter data proves to be consistent with our twin experiments. Indeed, the analyses show a better detachment of the Gulf Stream, weaker regional biases and more accurate positions for meso-scale structures. Independent hydrographic data (Argo floats and CTD cruises) and transports estimates along the OVIDE 2002 cruise show an improvement of the analysed oceanic state with respect to the assimilation-free case though water mass properties are still incorrectly represented. After assimilation, the North Atlantic heat transport in the model is in good agreement with independent estimates based on hydrographic data. (C) 2011 Elsevier Ltd. All rights reserved.