To improve the robustness of speech recognition in additive noisy environments, an SVD based space transformation approach is proposed. It is shown that with this approach, not only the signal-to-noise ratio is improved but also a significant recognition error reduction is achieved. A multiple model based on the proposed method is developed and it can provide high recognition rate for a large range of SNRs. Recognition experiments on a speaker-dependent mono-syllabic database with additive noise show that, this new approach outperforms LPC cepstrum, MFCC, and OSALPC cepstrum significantly.