A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO(2)) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO(2) climatology, and (2) the reconstruction of pCO(2) anomalies with respect to the climatology. For the first step, a grid-ded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO(2) and the ocean predictors. It provides monthly surface ocean pCO(2) distributions on a 1 degrees x 1 degrees grid for the period from 2001 to 2016. Global ocean pCO(2) was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO(2) with reasonable skill over the equatorial Pacific associated with ENSO (the El Nino-Southern Oscillation). Our model was compared to three pCO(2) mapping methods that participated in the Surface Ocean pCO(2) Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO(2) and sea-air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.