Active research in blind SIMO (Single Input Multiple Output) channel identification has led to a variety of second order statistics based algorithms, particularily the Subspace and the Linear Prediction approaches. The Subspace algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the Linear Prediction algorithm is sensitive to observation noise while its robustness to channel order over estimation is not always verified when the channel statistics are estimated. We propose a new second order statistics based blind channel identification algorithm which is truly robust to channel order over estimation i.e., able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the Linear Prediction algorithm. These facts are justified theoretically and verified through simulations.