Efficient health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. Health diagnosis using the developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as the member algorithms. The proposed classification fusion approach is demonstrated with a bearing health diagnostics problem. Case study results indicated that the proposed approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.