Progress in seasonal adjustment depends on the development not only of methods that better account for the various components of time series, but also the development of better diagnostics. A successful seasonal adjustment can depend as much on the diagnostics as on the methods. In this paper, we try to identify promising diagnostics for model-based seasonal adjustment.