Considering the increase in the deployment of wind energy conversion systems, improving the coexistence between wind turbines and wildlife with an efficient method for blade impact assessment is of primary importance. Automated blade-impacts on potential wildlife monitoring can support the development and operations of wind farms. A substantial challenge is represented by the typical case of impacts vibrations signature embedded in the operational vibrations of the wind turbine. A heterogeneous multisensor system for automatic eagle detection and deterrent, including an automatic blade-event detection module, was developed providing the necessary field data. An automated blade event detection system, based on support vector machine, a form of machine learning, was developed and tested. Training of the algorithm was performed using features extracted from vibration signals and energy distribution graphs obtained from numerical simulations of blade impacts. Performance of the method, evaluated using numerical simulations at different levels of signal-to-noise ratios, relative to artificial impacts, showed the best results when trained using combined raw vibration signal and time marginal integration graphs, exhibiting an overall accuracy of 93% at SNR=6. The proposed model was tailored for improving specificity (i.e., false negative error), a critical aspect for endangered species events. Performance of the trained algorithm evaluating field data exhibited an improvement in impact detection from a visually identifiable rate of 42% to true positive prediction rate of 75%. The system could perform, with appropriate training, diverse functions as components health monitoring or lighting strike automatic monitoring.