Tools based on multivariate statistical analysis are becoming very popular for automatically revealing the existence of damage in structures using vibration data under changing environmental and operational conditions (typically temperature, humidity and traffic intensity). In this paper, methods of multivariate statistical analysis are newly applied for monitoring the structural health state of the main cables of suspension bridges, with the main contribution of removing non-linear aeroelastic correlations between dynamic features and wind speed. Considering natural frequencies as damage-sensitive features, an analytical parametric model of suspension bridge with damage in one main cable and subjected to wind loading is formulated, at first, as an extension of previous work. Model predictions demonstrate that apparent frequency variations caused by changes in incoming wind speed can likely be more significant than those produced by a small damage. A technique based on principal component analysis and novelty detection is adopted to cope with this issue and its application to long-term pseudo-experimental buffeting response data, generated by means of the analytical model, is presented. The results demonstrate the feasibility of permanent monitoring systems to reveal the existence of damages in wind-excited long-span bridges producing relative variations in the most sensitive natural frequency smaller than 0.1%. (C) 2015 Elsevier Ltd. All rights reserved.