Managers of migratory striped bass stocks that mix along the Atlantic coast of the USA require periodic estimates of the relative contributions of the individual stocks to coastal mixed-stock fisheries; however, to date, a standard approach has not been adopted. We compared the performances of alternative stock identification approaches, using samples taken from the same sets of fish. Reference (known) samples were collected from three Atlantic coast spawning systems: the Hudson River, Chesapeake Bay, and the Roanoke River. Striped bass of mixed-stock origin were collected from eastern Long Island, New York, and were used as test (unknown) samples. The approaches applied were discriminant analysis of morphometric data and of meristic data, logistic regression analysis of combined meristic and morphometric data, discriminant analysis of scale-shape features, discriminant analysis of immunoassay data, and mixed-stock analysis of mitochondrial DNA (mtDNA) data. Overall correct classification rates of reference samples ranged from 94% to 66% when just the Hudson and Chesapeake stocks were considered and were comparable when the Chesapeake and Roanoke stocks were grouped as the “southern” stock. When all three stocks were treated independently, correct classification rates ranged from 82% to 49%. Despite the moderate range in correct classification rates, bias due to misallocation was relatively low for all methods, suggesting that resulting stock composition estimates should be fairly accurate. However, relative contribution estimates for the mixed-stock sample varied widely (e.g., from 81% to 47% for the Hudson River stock, when only the Hudson River and Chesapeake Bay stocks were considered). Discrepancies may be related to the reliance by all of these approaches (except mtDNA) on phenotypic features. Our results support future use of either a morphometrics-based approach (among the phenotypic methods) or a genotypic approach based on mtDNA analysis. We further recommend a conservative strategy of reliance on a single approach in tracking changes in relative contributions of striped bass stocks to coastal fisheries.