This paper proposed to apply senone log-likelihood ratio based articulatory features (AFs) to improve pronunciation erroneous tendency (PET) detection performance. The feedback information of articulation-placement and articulation-manner could be derived from the definition of PET. The framework of the method involved two main steps. 1) A bank of attribute extractors based on neural networks were trained to estimate the log-likelihood ratio (LLR) for each senone at a frame level. 2) AFs composed of those LLRs outputted from each attribute extractor were used for detecting PETs. Results demonstrated that the proposed system had better performance than the baseline system using MFCC. Moreover, substantial improvements were obtained by combining AFs with MFCC, achieving a lower false rejection rate of 5.0%, a lower false acceptance rate of 30.8% and a higher diagnostic accuracy of 89.8%.