Knowledge acquisition has been a major bottleneck in building expert systems. To ease the problems arising in knowledge acquisition, analog concept learning systems(ACLS) has been used. In this paper, in order to avoid the overfitting problem and secure a good performance, we propose the revised ACLS, which pruning methods -cost complexity, reduced error, pessimistic pruning and production rule- are incorporated into and apply them to the credit evaluation for Korean companies. The performances of the revised ACLS are evaluated in light of the prediction accuracy. To check the effect of the training data sampling on the performance, experiments are conducted using the different proportion of the training data. Experimental results show that the revised ACLS of combining cost complexity pruning with reduced error pruning performs best among original ACLS and other methods.