The effective wind speed for wind turbine can not be measured directly. Soft sensor modeling for effective wind speed was proposed based on support vector regression (SVR), and the effective wind speed was estimated by using the SVR-based model that relates the corresponding variable with other measurements. SVR is used to construct the soft-sensor model and sequential minimal optimization (SMO) is employed to train the model. The computer simulation results show that the proposed soft sensor model has two advantages of good generalization ability and high computation efficiency. And so it satisfies the large-scale variety of wind speed and real-time control requirements of wind turbine.