To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-line. The mapping relationship between a feature space and a product quality space can be built by using regression analysis, and in applying clustering analysis the product quality space can be partitioned automatically. Eventually, estimating product quality on-line can be accomplished by sorting the mapped data in the partitioned quality space. A concrete problem is proposed which has a relatively small ratio of training data to input variables. By implementing the method mentioned above, a satisfying result has been achieved. Furthermore, the further question about choosing suitable mapping methods is briefly discussed. To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-line. The mapping relationship between a feature space and a product quality space can be built by using regression analysis, and in applying clustering analysis the product quality space can be partitioned automatically. Eventually, estimating product quality on-line can be accomplished by sorting the mapped data in the partitioned quality space. has a relatively small ratio of training data to input variables. By implementing the method mentioned above, a further improvement has been achieved.