Breeding performance of seabirds reflects conditions in the marine environment, and seabirds are often considered suitable indicators because they are sensitive to variations in food supply and relatively easy to observe. However, any individual parameter (e.g. breeding success of a particular species at one site) may also be affected by drivers other than food supply, and selecting a suitable univariate indicator can be difficult, particularly if independent estimates of food availability on the appropriate scale are unavailable. We propose combining several data sets to overcome this limitation: if a given temporal pattern occurs for several parameters measured at one site, or for the same parameter measured at several sites, it is likely to reflect important spatiotemporal environmental variation, probably linked to food supply. Multivariate statistical techniques, such as principal component analysis (PCA), can be used to extract common signals from a number of intercorrelated time series. Examples from seabirds in the North Sea demonstrate that such common signals are correlated with physical and biological environmental variables. We propose a preliminary ‘North Sea seabird index’ and discuss how this index could be used in ecosystem-based management.