A major problem in the correct diagnosis of pulpal pain is that the associated clinical signs do not predictably correlate with the underlying pathological process. Using conditional probabilities of various pulp conditions from published data, Bayesian Statistical Inference provides the means for deriving a composite probability of the presence of a disease from a multiple set of symptoms. A computer program that can infer a diagnosis for pulpal pain from any combination of 17 clinical symptoms has been developed. From the data, the program provides the computed relative probabilities of a healthy pulp, a saveable pulp, an unsaveable pulp, and a necrotic pulp being present.