GONO is a numerical wave prediction model used for the preparation of forecasts as well as hindcasts. It is a hybrid model: the wind sea is described in a parametric way, but swell is treated in a spectral manner. For the wind sea there are two prognostic parameters: the zero-moment wave height and the mean direction. Pure wind-sea spectra are assumed to have a quasi-universal shape: above the spectral peak, an f-5 behavior is assumed; below the peak, linear frequency dependence is taken. The directional dependence is of the cos2 &thgr; type. Empirical relations are used to derive the full set of wind-sea parameters from the prognostic variables and the wind vector. The equations for the prognostic variables are solved on a discrete grid with the help of a simple finite-difference scheme. For the accurate propagation of swell, possibly over large distances, a ray technique is used. The full two-dimensional spectrum is reconstructed for selected gridpoints for which the results of the ray technique and the wind-sea calculations are combined. The model is a shallow water model because bottom dissipation effects are taken into account, but effects of refraction are disregarded. Depending on wind speed, these effects may be important in areas where the depth is less than about 100 m. The model has not been applied in regions with depths less than 15 m, therefore extreme shallow water effects are not considered. The behavior of the model was studied in quite some detail during the recent Sea Wave Modeling Project (SWAMP) in a few idealized situations. Knowledge of the model behavior in more realistic situations stems from its routine operational application. Runs are made four times a day on a grid covering the North Sea and the Norwegian Sea, and the results are monitored continuously. Presently, we have a data base containing about four years of observations and model predictions. From this data base we discuss a few interesting storms, and we present a statistical analysis of all of the available material. As part of this analysis we consider the effect of the quality of the input winds on the model performance.