Local sea-level is affected by a number of forcing factors, which all contribute to the trends observed by tide gauges. Here we use the fingerprints of main factors contributing to secular sea-level trends to construct an initial empirical model that explains best the trends in sea-level as recorded by the large number of coastal tide gauges over the last 50 years. The forcing factors considered include steric changes derived from observations, post-glacial rebound as predicted by geophysical models and mass changes in the Greenland and Antarctic ice sheets as predicted by the static sea-level equation. The approximation of the observed spatial pattern of sea-level trends through a model based on the spatial fingerprints of the main forcing factors fully utilizes the information contents of the available observations and models and allows the interpolation of the sea-level trends between the tide gauges. As a result, we obtain the global picture of sea-level trends due to the forcing factors accounted for in the analysis. Moreover, we derive constraints on the mass changes of the large ice sheets. The empirical models explain about 15% of the variance of the sea-level trends. Nevertheless, the models are correlated with the observations on the level of 0.38+/-0.07, indicating that most of the unexplained variance is due to contributions with small spatial scales. Averaged over the last five decades, the results indicate that the Antarctic and Greenland ice sheets have been melting with an equivalent contribution to global sea-level rise of 0.39+/-0.11 and 0.10+/-0.05 mm yr(-1), respectively. The steric signal derived from observations is clearly identified in the sea-level trends and is found to be at a minimum of 0.2 mm yr(-1), with the most likely value being close to 0.35 mm yr(-1). The global tide gauge network, which covers only a small fraction of the ocean surface, appears to sense an average sea-level rise larger than the global average. Extrapolating the regression models to the global ocean and taking into account the uncertainties in the extrapolation results in a most likely global average of the order of 1.05+/-0.75 mm yr(-1).