Quantitative estimation of surface ocean productivity and bottom water oxygen concentration with benthic foraminifera was attempted using 70 samples from equatorial and North Pacific surface sediments. These samples come from a well defined depth range in the ocean, between 2200 and 3200 m, so that depth related factors do not interfere with the estimation. Samples were selected so that foraminifera were well preserved in the sediments and temperature and salinity were nearly uniform (T=1.5 ¿C; S=34.6%0). The sample set was also assembled so as to minimize the correlation often seen between surface ocean productivity and bottom water oxygen values (r2=0.23 for prediction purposes in this case). This procedure reduced the chances of spurious results due to correlations between the environmental variables. The samples encompass a range of productivities from about 25 to >300 gC m-2 yr -1, and a bottom water oxygen range from 1.8 to 3.5 ml/L. Benthic foraminiferal assemblages were quantified using the >62 μm fraction of the sediments and 46 taxon categories. MANOVA multivariate regression was used to project the faunal matrix onto the two environmental dimensions using published values for productivity and bottom water oxygen to calibrate this operation. The success of this regression was measured with the multivariate r2 which was 0.98 for the productivity dimension and 0.96 for the oxygen dimension. These high coefficients indicate that both environmental variables are strongly imbedded in the faunal data matrix. Analysis of the beta regression coefficients shows that the environmental signals are carried by groups of taxa which are consistent with previous work characterizing benthic foraminiferal responses to productivity and bottom water oxygen. The results of this study suggest that benthic foraminiferal assemblages can be used for quantitative reconstruction of surface ocean productivity and bottom water oxygen concentrations if suitable surface sediment calibration data sets are developed and appropriate means for detecting no-analog samples are found. ¿ American Geophysical Union 1994