The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood״ function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model, Self-Organizing Mixture Map, and have some favoured attributes on its robust convergence and good generalization. The experimental results on both artificial and real market data show that the algorithm is a good alternative in measuring multimodal distribution.