Appropriate and correct indoor positioning in wireless networks could provide interesting services and applications in many domains. There are time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and location fingerprinting schemes that can he used for positioning. We focus on location fingerprinting in this paper since it is more applicable to complex indoor environments than other schemes. Location fingerprinting uses received signal strength to estimate locations of mobile nodes or users. Probabilistic method, k-nearest-neighbor, and neural networks are previously proposed positioning techniques based on location fingerprinting. However, most of these previous works only concentrate on accuracy, which means the average distance error. Actually, it is not enough to measure the performance of a positioning technique by the accuracy only. A comprehensive performance comparison is also critical and helpful in order to choose the most fitting algorithm in real environments. In this paper, we compare comprehensively various performance metrics including accuracy, precision, complexity, robustness, and scalability. Through our analysis and experiment results, k-nearest-neighbor reports the best overall performance for the indoor positioning purpose.