Energy recovery from municipal solid waste (MSW) is a feasible method through various processes, such as combustion, pyrolysis and gasification. Tests on the co-firing of MSW and coal were conducted in a 0.2 MWth circulating fluidized bed, and the emissions of NO, N2O, HCl and SO2 were studied. A three layer feed forward neural network was constructed and trained by the BP method with experimental data. The impacts of mixing ratio and bed temperature on the gaseous emissions were considered. The model predicted gaseous pollutions emissions were consistent with experimental data.