In the traditional Bayesian classifiers such as the Gaussian discriminant analysis method and the Naive Bayesian method, the correlation between variables are commonly simplified when constructing the joint probability distribution of variables. Accordingly, the estimation of the class conditional probability density would have differences with the actual data. In this study, a Bayesian classifier based on the D-vine Copula theory is developed by investigating on the correlation between variables. The main objective is to improve the accuracy of the class conditional probability density estimation. The joint probability distribution of variables is decomposed into a series of pair Copula functions and marginal probability density functions. The kernel function method is adopted to estimate the marginal probability density. The parameters of pair Copula functions are optimized by the maximum likelihood estimation. The developed method is analyzed and validated on the classification of neurophysiological signals. The obtained results show that it has better performance on several classification indexes.