We have already proposed a new feature extraction method based on higher-order local auto-correlation and Fisher weight map (FWM) at Interspeech 2006. This papershows effectiveness of the proposed FWM in speaker dependent and speaker independent phoneme recognition. Widely used MFCC features lack temporal dynamics. To solve this problem, local auto-correlation features are computed and accumulated by weighting high scores on the discriminative areas. This score map is called Fisher weight map. From the speaker dependent phoneme recognition, the proposed FWM showed 79.5% recognition rate, by 5.0 points higher than the result by MFCC. Further more by combing FWM with MFCC and delta-MFCC, the recognition rate improved to 88.3%. In the speaker independent phoneme recognition, it showed 84.2% recognition rate, by 11.0 points higher than the result by MFCC. By combining FWM with MFCC and delta-MFCC, the recognition rate improved to 89.0%.