This paper describes a syntax-directed real-time connected-word recognition system and compares the performance of different recognition algorithms. This is a speaker dependent system based on whole word template matching, and the recognition is done using a one-pass dynamic time-warping algorithm. The symmetric local constraints of Sakoe and Chiba are used for connected word recognition, and with proper normalization of the accumulated distances, result in better performance than the Itakura local constraints. By allowing a noise template to start and end the sentences during the recognition process, most of the errors due to a bad endpoint detection are eliminated. When memory and computational resources are limited, vector quantization allows more templates for each word in the dictionary, and as a consequence, the recognition performance increases. By carefully implementing the algorithm on standard hardware (VAX 11/780 and FPS AP-120B), real-time recognition is achieved for vocabularies of up to 100 templates, or up to 250 templates if vector quantization is used.