In this paper, we proposed a speech signal denoising method using common vector approach (CVA) for very noisy signals. Based on CVA, classes are constructed from windowed speech samples/vectors according to their characteristics and denoising is performed on difference vectors. Speech signal is then reconstructed using denoised patches. Size of windows and amount of overlapping are important parameters in this method, affecting the performance/noise reduction ratio. CVA results are compared against the results of Geometric Approach and Magnitude Squared Spectrum methods according to well-known performance measures. Experimental results show that the proposed common vector based method is superior to other techniques for very noisy speech signals. Method is open to enhancement with different parameters and noise estimation methods.