In this paper, we present a VQ-based fast face recognition algorithm using an optimized codebook. Previously, Chen et al.  proposed a novel codebook design method based on the systematic classification and organization of code patterns abstracted from facial images for reliable face recognition. In this paper, an improved codebook design method is proposed. Combined by a systematically organized codebook based on the classification of code patterns and another codebook created by Kohonen's Self-Organizing Maps (SOM), an optimized codebook consisted of 2x2 codevectors for facial images is generated. We demonstrate the performance of our algorithm using publicly available AT&T database containing variations in lighting, posing, and expressions. Compared with the algorithms employing original codebook or SOM codebook separately, experimental results show face recognition using the optimized codebook is more efficient. The highest average recognition rate of 98.2% is obtained for 40 persons' 400 images of AT&T database. A table look-up (TLU) method is also proposed for the speed up of the recognition processing in this paper. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.