Zusammenfassung: Often only simple relative abundance time series and basic growth and (or) survival estimates are available for assessing impacts of fishing and environmental factors. Assessment then involves fitting production models to the series, while forcing the model with observed catch or effort series. A key uncertainty in this approach is how to deal with recruitment variations due to factors other than stock size. A dynamic programming algorithm can be used to compute maximum likelihood estimates of the recruitment anomaly sequence, given prior knowledge of growth parameters, the natural survival rate, and proportion of the variation in the relative abundance index that is due to abundance measurement errors.