Abstract: In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. We need to determine the different patterns of susceptibility. In this paper we model the number of susceptibility's patterns and the parameters jointly, and base inference about these quantities on their posterior probabilities, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. For convenience, we always assume different patterns of susceptibility have common variances. The paper apply the methodology to the analysis of univariate normal mixtures with different variances. The practical significance of the proposed method is illustrated with a dose-response data set.