Additional information concerning the quality of growing stock in forests has been obtained for the first time in Baden-Württemberg and Rheinland-Pfalz during the course of Germany’s second national forest inventory (BWI 2; conducted during the period 2001–2002). In this article, the quality assessment—called stem quality rating method—is described with a special focus on its potential to provide the basis for a more detailed investigation of the growing stock’s quality distribution. As a main result, the article presents and illustrates a model-based quantification of single tree and stand/site variable effects on the quality distribution of Norway spruce. Single tree variables showing a significant effect are diameter at breast height (DBH), height–DBH-ratio (h/d-value), age and distance to forest edge. Additional stand/site variables which have a significant effect are altitude, terrain slope, stand type and inventory team. Due to the ordinal type of the response variable, a categorical regression model is applied. Non-linear effects of predictor variables were detected and modeled by integration of smoothing spline terms. Validating model predictions with regard to expert knowledge in forestry led to the integration of simple constraints in the linear predictor, which controls whether category-specific effects are fitted or not. The resulting model could be described as a vector generalized additive non-proportional odds regression model. This improved insight into the determination of stem quality could be applied in optimization studies to derive optimal silvicultural treatments and in the setting up of management guidelines. Assuming constant relationships between predictor and response variables over time, the combined application with growth simulators allows for a prediction of future joint quality and size class assortment distributions. Finally, the model would allow for a sustainability control of stem quality over time if a consecutive inventory will be conducted during the course of the third German national forest inventory (BWI 3).