Entropy based sensor selection heuristics is proposed for localization applications. Given 1) the prior target location distribution, and 2) the location and sensing uncertainty of a set of sensors, the heuristics selects a sub optimal sensor whose measurement would yield nearly the greatest uncertainty reduction of the target location probability distribution. The heuristics defines the potential of a sensor to reduce target location distribution uncertainty. The potential is positively proportional to the entropy of the sensors view of the target location distribution. The potential is negatively proportional to the sensors sensing uncertainty. All candidate sensors potential are evaluated without retrieving actual sensor measurements. The heuristics is illustrated with a localization case study in which bearing sensors, range sensor and time difference sensors are used.