Results of a single-blind round-robin comparison of satellite primary productivity algorithms are presented. The goal of the round-robin exercise was to determine the accuracy of the algorithms in predicting depth-integrated primary production from information amenable to remote sensing. Twelve algorithms, developed by 10 teams, were evaluated by comparing their ability to estimate depth-integrated daily production (IP, mg C m−2) at 89 stations in geographically diverse provinces. Algorithms were furnished information about the surface chlorophyll concentration, temperature, photosynthetic available radiation, latitude, longitude, and day of the year. Algorithm results were then compared with IP estimates derived from 14C uptake measurements at the same stations. Estimates from the best-performing algorithms were generally within a factor of 2 of the 14C-derived estimates. Many algorithms had systematic biases that can possibly be eliminated by reparameterizing underlying relationships. The performance of the algorithms and degree of correlation with each other were independent of the algorithms’ complexity.