The contribution of contaminated sediments to effects on sediment-dwelling organisms (including plants and invertebrates), aquatic-dependent wildlife (amphibians, reptiles, fish, birds, and mammals), and human health has become more apparent in recent years. Sediments can serve both as reservoirs and as potential sources of contaminants to the water column and can adversely affect sediment-dwelling organisms by causing direct toxicity or altering benthic invertebrate community structure. Although the results of sediment toxicity tests and benthic invertebrate community assessments can be used directly to evaluate or infer effects on resident sediment-dwelling organisms, effective interpretation of sediment chemistry data requires tools that link chemical concentrations to the potential for observing adverse biological effects. This report describes the development of logistic regression models that quantify relationships between the concentrations of contaminants in field-collected sediments and the classification of samples as toxic on the basis of tests using two species of marine amphipods, Rhepoxynius abronius and Ampelisca abdita. Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability that a sample would be classified as toxic. These models were derived from a large database of matching sediment chemistry and toxicity data that includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models. The individual chemical regression models were combined into a single model to estimate the probability of toxicity on the basis of the mixture of chemicals present in a sample. The average predicted probability of toxicity closely matched the observed proportion of toxic samples within the same ranges, demonstrating the overall reliability of the P(underline)Max model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased.