These days, cheap and intelligent sensors, networked through wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and controlling homes, cities and the environment. Networked sensors also offer a broad range of applications. Localization capability is essential in most wireless sensor networks applications; for instance in environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are meaningless without accurate knowledge of where they are obtained. Localization techniques are used to determine location information by estimating the location of each sensor node. Distance measurement errors are commonly known to affect the accuracy of the estimated location; resulting in errors that may be due to inherent or environmental factors. Trilateration [1] is a well-known method for localizing nodes by using the distances to three anchor nodes; yet it performs poorly when they are many distance measurement errors. Therefore, we propose the LRD (Localization with Ratio-Distance) algorithm, which performs strongly even in the presence of many measurement errors associated with the estimated distance to anchor nodes. Simulations using the OPNET Modeler show that LRD is more accurate than trilateration.