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Spectral pattern recognition (SPR) methods are among the most powerful tools currently available for noninvasively examining the spectroscopic and other chemical data for environmental analysis and monitoring. Using spectral data, these systems have found a variety of applications in chemometric systems such as gas chromatography, fluorescence spectroscopy, etc. An advantage of SPR approaches is that they make no a priori assumption regarding the structure of spectra. However, a majority of these systems rely on human judgment for parameter selection and classification. We considered a SPR problem as a composite of five subproblems: pattern acquisition, feature extraction, feature selection, knowledge organization, and pattern classification. One of the basic issues in SPR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used for classification. Various features present in a pattern and a large variety of classification algorithms could be used. A spectral pattern classification system combining the above components and multivariate decision-theoretic approaches for classification is developed. It is shown how such a system can be used for large data analysis, warehousing, and interpretation. In a preliminary test, the system was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.