Atlantic Coast striped bass (Morone saxatilis) commercial fisheries data are examined for periodicities in the appearance of dominant year-classes using autocorrelation and spectral-analysis techniques. Results obtained using maximum entropy and classical Fourier spectral-analysis methods are compared. Statistically significant periodicities of approximately 20 yr and of 6–8 yr are common to the time series for most states and regions. Since the periodicities are neither very pronounced nor simple, it is difficult to isolate the causative factors, which are more likely to be density-independent environmental factors enhancing survival of the young than intrinsic characteristics of the life cycle of striped bass. The spectra for Maryland landings and Maryland landings per unit gear are nearly identical, suggesting that at least for Maryland the landings and landings-per-unit-gear data are approximately equally reliable as indices of stock size. The structure of the North Carolina time series is unique, which supports the opinion that this stock does not mix appreciably with Chesapeake and Hudson stocks. Impact assessments and monitoring programs should not be predicted on the expectation of pronounced or simple periodicities of 6 years or any other time interval in the appearance of dominant year-classes in Atlantic Coast striped bass populations.Key words: autocorrelation analysis, commercial fisheries data, Fourier spectral analysis, maximum entropy spectral analysis, periodicities, striped bass