Dysarthria is a collection of motor speech disorder. A severity of dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R2 = 0.74) and 2.5% (R2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11 % (R2 = 0.48) for HMM.