In　this　paper,　a　data-driven　subspace　approach　for　economic　performance　assessment　of　the　advanced　process　control　(APC)　systems　is　presented.　The　method　introduces　LQG　tradeoff　curve　to　estimate　potential　of　reduction　in　variance,　which　is　directly　obtained　from　subspace　matrices　using　closed　loop　data.　To　exploit　feasible　economic　performance　of　the　APC　systems,　the　proposed　approach　considers　the　uncertainties　induced　by　process　variability　and　evaluates　the　economic　performance　through　solving　stochastic　optimization　problem.　Results　of　the　performance　evaluation　provide　a　guideline　for　the　control　system　tuning　to　realize　the　potential　improvement　in　profitability　of　process.　The　application　of　the　proposed　method　is　illustrated　by　its　benefits　evaluation　on　a　simulated　example.