This paper presents a review of current research carried on at various laboratories aiming to increase the robustness of speech recognition systems to channel and microphone variations. A comparative analysis of several techniques, used in recent studies on microphone-independence, are discussed and compared: these include Cepstral High- Pass Filtering, Cepstral-Mean Normalization, Ratz algorithm and Bayesian learning. Also, some results obtained at CSELT labs using the methods above mentioned are reported, specifically addressing the issue of robustness of ASR systems to microphone variations.