Most of the existing attribute selection methods did not consider the cost of attribute extraction and automatic decision of the dimension of attribute subset. In this paper, a novel approach called satisfactory attribute selection method (SASM)is proposed which considers compromisingly classification performance of attribute samples, the dimension of attribute set and the complexity of attribute extraction. Attribute satisfactory rate and attribute set satisfactory rate are defined. Several satisfactory rate functions are designed. Satisfactory attribute set evaluation criterion is given in a mathematical way. Satisfactory attribute selection algorithm is described in detail. Experimental results ofcustomer churn prediction for a telecommunication carrier show that SASM is superior to correlation selection, consistency selection, instancebased selection and symmetric uncertainty selection in hit rate, covering rate, accuracy rate and lift coefficient. Hence, the validity and applicability of the proposed method are verified.