Stellar spectra classification is an indispensable part of any workable automated recognition system of celestial bodies. Like other celestial spectra, stellar spectra are also extremely noisy and voluminous; consequently, any acceptable technique of classification must be both computationally efficient and robust to structural noise. In this paper, we propose a practical stellar spectral classification technique which is composed of the following three steps: In the first step, the Haar

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... transform is used to extract spectral lines, then followed by a de-noising process by the hard thresholding in the wavelet field. As a result, in the subsequent steps, only those extracted spectral lines are used for classification due to the high reliability of spectral lines with respect to the continuum. In the second step, the Principal Component Analysis (PCA) is employed for optimal data compression. More specifically, we use 165 well-selected samples from 7 spectral classes of stellar spectra to construct the "eigen-lines spectra" by PCA. Thirdly, unknown spectra are projected to the eigen-subspace defined by the above eigen-lines spectra, and then a fuzzy c-means algorithm is used for the final classification. The experiments show that our new technique is both robust and efficient.