The mismatch between training and testing environments makes the necessity of speech recognizers to be adaptive both in acoustic modeling and decision making. Accordingly, the speech hidden Markov models (HMMs) should be able to incrementally capture the evolving statistics of environments using online available data. Also, it is necessary for speech recognizers to exploit the robust decision strategy, which takes the uncertainty of parameters into account. This paper presents a transformation-based Bayesian predictive classification (TBPC) where the uncertainty of the transformation parameters of the HMM mean vector and precision matrix is adequately represented by a joint multivariate prior density of normal-Wishart belonging to the conjugate family. The formulation of TBPC decision is correspondingly constructed. Due to the benefit of conjugate density, we generate the reproducible prior/posterior pair such that the hyperparameters of prior density could evolve successively to new environments using online test/adaptation data. The evolved hyperparameters could suitably describe the parameter uncertainty for TBPC decision. Therefore, a novel framework of TBPC geared with online prior evolution (OPE) capability is developed for robust speech recognition. This framework is examined to be effective as well as efficient on the recognition task of connected Chinese digits in hands-free car environments.