We describe and compare two probabilistic models for task-specific seafloor characterization based on multispectral backscatter. We examine whether generative or discriminative approaches to supervised seafloor characterization do better at harnessing the greatly increased information about seafloor substrate composition that is encoded in the backscattering response across multiple frequencies. A Gaussian mixture model (GMM) is proposed as a generative model, and a fully-connected conditional random field (CRF) is proposed as a discriminative model. Either model uses input data derived from monospectral or multispectral backscatter without modification. The CRF approach considers both the relative backscatter magnitudes of different substrates as well as their relative proximity, and can be optimized using parameters. The GMM model, in contrast, includes no spatial information in its estimates, being based solely on relative backscatter magnitudes. Both GMM and CRF modeling approaches perform better with multispectral backscatter compared to monospectral, significantly outperforming all three monospectral frequencies. With multispectral backscatter inputs, based on average classification accuracies alone, there was little to choose between the two modeling approaches (classification accuracy of 81% and 83% for GMM and CRF models, respectively, evaluated using 50% of available bed observations to train and 50% to test the models). However, a CRF model that has been optimized with respect to its tunable parameters tends to produce higher posterior probabilities (i.e. greater certainty) for its classifications. Using monospectral backscatter inputs, the CRF model significantly outperformed the GMM model in terms of average classification accuracy. On balance, therefore, based on the evidence presented here, the CRF is suggested to be the superior approach for task-specific seafloor classification. Although further work using additional data is required to further examine this conclusion, the work presented here will guide and focus subsequent research efforts as more areas of the seafloor are mapped with the new technology. In order to facilitate these efforts, the algorithms presented here are encoded in a freely available python toolbox for Probabilistic acoustic Sediment Mapping, called PriSM , that can be used for both monospectral and multispectral backscatter. Finally, we show that application of the CRF model to the outputs of a geoacoustical model of seafloor scattering results in realistic substrate classification boundaries. This hybrid CRF and physics-based approach can predict the physical properties of the seafloor at a finer spatial resolution than is possible using the geoacoustical model alone.