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Speech enhancement is widely used to improve the perceptual quality of noisy speech by suppressing the interfering ambient noise and is commonly evaluated via objective quality measures. Automatic speech recognition (ASR) systems also use such speech enhancement technologies in front-end to improve their noise robustness. If the objective measures have a high correlation with speech recognition accuracy, we can effectively predict the ASR performance according to objective quality measures in advance and flexibly optimize the enhancement algorithms in the stage of system design. Motivated by such idea, this paper investigates the correlation between the ASR performance and several traditional objective measures based on Aurora2 database. In the Experimental results the highest correlation coefficient, 0.962, is provided by weighted spectral slope measure (WSS).