In this paper, we study issues related to the use of online incremental adaptation for speech recognition. We use the segmental MAP algorithm to perform HMM adaptation. Two modes of incremental adaptation, namely supervised and unsupervised adaptation, are tested, in the supervised mode, the correct word transcription is used for MAP adaptation while in the unsupervised mode, the word transcription is provided by the recognizer. We report on results obtained for the DARPA Air Travel Information System (ATIS) task. Compared with the speaker independent recognition results, the supervised and unsupervised incremental adaptation algorithms reduce the word error rate by 25% and 5.6% respectively. More study is needed in improving adaptation efficiency and effectiveness. More study is also needed in bridging the gap between supervised and unsupervised adaptation.