We investigated the speech recognition of a person with articulation disorders resulting from athetoid cerebral palsy. Recently, the accuracy of speaker-independent speech recognition has been remarkably improved by the use of stochastic modeling of speech. However, the use of those acoustic models causes degradation of speech recognition for a person with different speech styles (e.g., articulation disorders). In this paper, we discuss our efforts to build an acoustic model for a person with articulation disorders. The articulation of the first speech tends to become unstable due to strain on muscles and that causes degradation of speech recognition. Therefore, we propose a robust feature extraction method based on PCA (Principal Component Analysis) instead of MFCC. Its effectiveness is confirmed by word recognition experiments.