Due to the decline of fisheries throughout the world, there is an ever-increasing demand among fisheries managers for more and better data regarding the distribution and abundance of commercially important fishes. Along the Pacific coast of North America, there are insufficient stock data for most rockfish species, which compose one of the most valuable commercial and recreational fisheries in California. One approach being explored for increasing our understanding of fish distribution patterns and potentially generating stock assessment data over large areas is the use of habitat-based assessment. The general hypothesis is that because rockfish are not randomly distributed across habitats, it should be possible to model and predict their distribution and abundance based on habitat maps and biological data. Furthermore, to the extent that these models are robust and portable, they should be applicable across a variety of locations and physical settings. We attempt to test these hypotheses using predictive models created for two species of rockfish [Sebastes flavidus (yellowtail) and S. rosaceus (rosy)] within Cordell Bank National Marine Sanctuary (CBMNS). These models, created as part of a recent study, were applied to and tested against distribution data from a previous study of the two species in question at Del Monte shalebeds in Monterey Bay, California. The general linear models (GLMs) were created using rugosity, slope, aspect, depth, and topographic position index analyses of bathymetric digital elevation along with presence/absence data for the two species of rockfish. The model for S. flavidus generated at CBNMS proved to be at least as efficient at predicting yellowtail rockfish distribution at Del Monte as in the setting in which it was created, while the model for S. rosaceus failed to predict rosy rockfish distribution at Del Monte with any reliability.