A new modular recurrent neural network (MRNN)-based speech-recognition method that can recognize the entire vocabulary of 1280 highly confusable Mandarin syllables is proposed in this paper. The basic idea is to first split the complicated task, in both feature and temporal domains, into several much simpler subtasks involving subsyllable and tone discrimination, and then to use two weighting RNN's to generate several dynamic weighting functions to integrate the subsolutions into a complete solution. The novelty of the proposed method lies mainly in the use of appropriate a priori linguistic knowledge of simple initial-final structures of Mandarin syllables in the architecture design of the MRNN. The resulting MRNN is therefore effective and efficient in discriminating among highly confusable Mandarin syllables. Thus both the time-alignment and scaling problems of the ANN-based approach for large-vocabulary speech-recognition can be addressed. Experimental results show that the proposed method and its extensions, the reverse-time MRNN (Rev-MRNN) and bidirection MRNN (Bi-MRNN), all outperform an advanced HMM method trained with the MCE/GPD algorithm in both recognition-rate and system complexity.