Due to the necessity of more sources of renewable wind generated energy, the number of wind turbines in the Netherlands has grown in the past. Unfortunately, big sizes and movements of the blades negatively impact ground-based Doppler radars in the form of Doppler clutter. Such clutter leads to a downgrade of the surveillance radar performance in airplanes and precipitation detection, their parameters estimation. Our goal is to characterize and possibly eliminate the Doppler clutter from the wind turbine, where polarization is used as an important tool to reach this goal. As Doppler frequency and polarization are key characteristics for this study, a theoretical model based on these characteristics is built to predict the behavior of the rotated rotor blades of the wind turbine which shows promising results for amplitude and phase analysis. To evaluate this model, real measurements have been done with the PARSAX radar, which gives possibility to represent the wind turbine data in Doppler-range domain or Doppler-time domain in terms of amplitudes and phases of all four elements of the polarization scattering matrix. Using orthogonal LFMwaveforms for simultaneous polarimetric measurements, despite of huge benefits, has one serious drawback a different residual phases in different polarimetric channels. Testing and analysis of a few algorithms, the use of zero Doppler frequency range profile of phases with different algorithms of phase unwrapping, for their estimation and compensation did not result, unfortunately, in reliable compensation of residual phases. As a result, the analysis of wind turbines clutter has been focused on analysis of amplitude polarization characteristics only. For an experimental study a wind turbine was selected, which is located in Zoeterwoude near a highway. Such location allows to observe scattering from wind turbine and automotive targets simultaneously at the same range distance, with the possibility of data comparison. The goal of data analysis is to find polarimetric features, which for both types of targets behave differently, there is a possibility to eliminate the clutter from the wind turbine only. For comparative analysis, the absolute terms of the averaged covariance matrix are used in terms of 2D histograms to find differences between the wind turbine and automotive targets. This is done for consecutive time frames, where the results show different and similar behavior depending on the time frame. Another approach is to obtain and compare the so called target Euler parameters, which are related to a physical of the specific target to extend our study, a few polarimetric decomposition techniques (Pauli and H/®) are used to study feasibility of targets classification. Using comparative analysis with the covariance matrix, shows great potential with correlation coefficient algorithms in combination with polarization. The results are promising, but vary as the correlation coefficient highly depends on the vehicle and the orientation angle of the blades. The results of direct estimation of Euler parameters and the H/® decomposition both show differences between vehicles and blades and therefore potential in distinguishing both targets. Though, the results are affected by the residual phase and therefore additional research is recommended on this problem for better reliability.