Vibrations of a wind turbine have a negative impact on its performance. Mitigating this undesirable impact requires knowledge of the relationship between the vibrations and other wind turbine parameters that could be potentially modified. Three approaches for ranking the impact importance of measurable turbine parameters on the vibrations of the drive train and the tower are discussed. They include the predictor importance analysis, the global sensitivity analysis, and the correlation coefficient analysis versed in data mining and statistics. To decouple the impact of wind speed on the vibrations of the drive train and the tower, the analysis is performed on data sets with narrow speed ranges. Wavelet analysis is applied to filter noisy accelerometer data. To exclude the impact malfunctions on the vibration analysis, the data are analyzed in a frequency domain. Data-mining algorithms are used to build models with turbine parameters of interest as inputs, and the vibrations of drive train and tower as outputs. The performance of each model is thoroughly evaluated based on metrics widely used in the wind industry. The neural network algorithm outperforms other classifiers and is considered to be the most promising approach to study wind turbine vibrations.