A new dimension reduction method based on correlation coefficient matrix is proposed aimed at the high-dimension of characteristic parameters in the process of pattern recognition for partial discharge in power transformer. The correlation coefficient matrix (CCM) is constructed using parameters extracted from partial discharge signal in power transformer. The parameters which have similar classification ability to each other are deleted with the help of correlation analysis among 18 characteristic parameters in CCM. Six parameters which have higher classification capabilities are extracted using the critical index and are used as the inputs for pattern classifiers of probabilistic neural networks. The results show that the parameter dimension is reduced and the classifier construction is simplified, and the recognition effect is better than that of the traditional back propagation neural network in the condition of small samples.