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Sedimentary microfacies recognition of Yueguifeng Formation in Lishui-Jiaojiang sag of East China Sea Shelf Basin is circumscribed because of the shortage of offshore drilling well core. Probabilistic neural network ( PNN) was used for sedimentary microfacies recognition of Yueguifeng Formation. Firstly, based on the response analysis of seismic facies-sedimenlary facies and principal component analysis of logging curve- relationship between seismic facies and sedimentary facies was coupling and corresponding, so that seismic facies was selected as the category independent variable from the input item parameters of PNN, meanwhile, natural gamma, self potential, acoustic, density logging, compensated neutron and caliper logging curves, which were helpful to distinguish different sedimentary microfacies, were taken as the numerical independent variables from the input item parameters of PNN; secondly, 2 199 learning samples were used to train PNN, the optimal smoothing factors of variables were searched after 65 tests, and the discrimination model for 20 kinds of sedimentary microfacies in study area was established; finally, the PNN established was used to identify sedimentary microfacies. The results show that compared with the result of well core analysis, the accuracy rate of sedimentary microfacies identified by PNN is more than 90%, so that it is feasible to identify sedimenlary microfacies from the non-cored area by PNN. [ABSTRACT FROM AUTHOR]