Abstract: In the context of localization for Computational Auditory Scene Analysis (CASA), probabilistic localisation is a technique where a probability that a sound source is present is computed for each possible direction. This approach has been shown to work well with binaural signals provided the location of the sources to be localized is in front of the user and approximately on the same plane as the ears. Modern hearing aids use multiple microphones to perform array processing, and in a bilateral configuration, the extra microphones can be used by localization algorithms to not only estimate the horizontal direction (azimuth), but vertical direction (elevation) as well, thereby also resolving the front-back confusion. In this work, we present three different approaches to use Gaussian Mixture Model classifiers to localize sounds relative to a multi- microphone bilateral hearing aid. One approach is to divide a unit sphere into a nonuniform grid and assign a class to each grid point; the other two approaches estimate elevation and azimuth separately, using either a vertical-polar coordinate system or an ear- polar coordinate system. The benefits and drawbacks in terms of performance, computational complexity and memory requirements are discussed for each of these approaches.