HarkMan keyword-spotter was designed so that it can be used in a real-world environment to automatically spot the given words of a vocabulary-independent (VIND) task in unconstrained Chinese telephone speech. In this spotter, the speaking manner and the number of keywords are not limited. This paper focuses on a novel technique that addresses acoustic modeling, keyword-spotting network, search strategies, robustness, and rejection adopted in HarkMan. The underlying technologies used in HarkMan given in this paper are not only for keyword spotting but also for continuous speech recognition, which had been proved very efficient. It achieved the figure-of-merit (FOM) value over 90%.