A Bayesian blind source separation (BSS) algorithm is proposed in this paper to recover independent sources from observed multivariate spatial patterns. As a widely used mechanism, Gaussian mixture model is adopted to represent the sources for statistical description and machine learning. In the context of linear latent variable BSS model, some conjugate priors are incorporated into the hyperparameters estimation of mixing matrix. The proposed algorithm then approximates the full posteriors over model structure and source parameters in an analytical manner based on variational Bayesian treatment. Experimental studies demonstrate that this Bayesian source separation algorithm is appropriate for systematic spatial pattern analysis by modeling arbitrary sources and identify their effects on high dimensional measurement data. The identified patterns will serve as diagnosis aids for gaining insight into the nature of physical process for the potential use of statistical quality control.