Vocal dysfunctions and pathologies can be devastating to one's ability to produce speech properly. A novel method for approaching the problem of speech pathology assessment is presented in this paper. The focus is not to detect or measure all possible pathologies, but rather to assess quality for the case where the probablility of pathology is high. The system is a screening test that combines objective quality measures that examine both excitation and vocal tract characteristics. The five integrated measures are pitch perturbation, amplitude perturbation, a main cepstral peak measure, the log-likelihood measure, and an energy-weighted log-likelihood measure. They are evaluated over six speech phoneme classes and their ability to assess the quality of speech is examined. Ultimately, these measures will be seamlessly integrated into an overall pathology assessment system using a hidden Markov model (HMM) recognizer. To demonstrate the ability of the quality measures to probe the multidimensional perceptual quality space, a neural network based speech pathology detection scheme was established. This system attained an average classification rate of 85.8% for healthy and pathology speech.