The problem of selecting an appropriate wavelet filter is always present in the wavelet-based compression. Different mother wavelets are characterized by their regularity, which describes the smoothness of the wavelet. Digital signals should be characterized similarly to enable the selection of a good wavelet filter. In this paper, certain features and cooccurrence matrix are used in characterizing the spectra. Bayesian classification is used to classify the spectra into the classes defined by the best wavelet filter obtained from the compression of the training spectra. A training set is obtained from three multispectral images. The results show, that our method gives the correct result in wavelet filter selection for multispectral image compression.