D-statistic contribution analysis has been frequently used in practice for fault diagnosis. Existing algorithms for computing contributions to D-statistic tend to distribute cross-term contribution equally between two correlated variables. This leads to increased variance in contribution estimation and hence poor separability of faulty and normal variables. A new method for contribution calculation to D-statistic is proposed here which introduces a weighting scheme capable of distinguishing the contributions of two correlated variables. Simulation examples show that the proposed approach achieves improved resolution for distinguishing faulty and normal conditions. © Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2011.