A new efficient approach to quantize the spectral line frequencies (LSF) in a coder is proposed. The use of the full search algorithm in the spectral parameters quantization causes high complexity and large hardware storage. Attempts to reduce the complexity have been performed by lowering the size of the LSF codebook. This option leads to a sub-optimal solution; the number of LSF vectors to be tested affects the performance of the speech coder. Cache codebook (CCB) technique enhances the search of the optimal quantized spectral information. In this technique the size of the main codebook is kept unchanged while the number of closest match searches is reduced. Unlike the classical quantizer design, the CCB method involves one main codebook embedding four disjoint sub-codebooks. The content of the CCB at any time is an exact reproduction of one of the four sub-codebooks. The search for the best match to an input vector is limited to the LSF vectors of the CCB. Some criteria are used to accept or reject this closest match. The CCB is updated whenever the decision is in favor of rejection. The cache codebook was successfully embedded in a CELP coder to enhance the quantization of the spectral information. The comparison simulation results show that the Codebook Caching approach yields to comparable objective and subjective performance to that of the optimal full-search technique when using the same training and testing database.