Raman spectroscopy has provided a powerful means of chemical identification in a variety of fields, partly because of its noncontact nature and the speed at which measurements can be taken. Given a library of known Raman spectra, a common detection approach is to first estimate the relative amount of each chemical present, and then compare the estimated mixing coefficients to a threshold. We present a more rigorous detection scheme by formulating the problem as one of multiple hypothesis detection and using the maximum a posteriori decision rule to minimize the probability of classification error. The probability that a specific target chemical is present is estimated by summing the estimated probabilities of all the hypotheses containing it. Alternatively, since we do not typically have reasonable priors for the hypotheses, it may be preferable to interpret the result as an abstract score corresponding to the minimum description length approach. The resulting detection performance of this approach is compared to that of several other classification algorithms.