Abstract: Different ultrasound echo properties have been used for noninvasive temperature monitoring. Temperature variations that occur during the heating/cooling process induce changes in a random process of ultrasound backscattering. It has already been proved that the probability distribution of the backscattered RF (radio frequency) signals is sensitive to temperature variations. Contrary to previously utilized methods, which explored models of scattering and involved techniques of fitting histograms into a special probability distribution, two more direct measures of changes in statistics as temperature markers are proposed in this paper. They measure the “distance” between probability distributions. The markers are the Kolmogorov Smirnov distance and Kulback-Leiber divergence. The feasibility of using such nonparametric statistics for noninvasive ultrasound temperature estimation is demonstrated in the ultrasounds data collected during series of heating experiments in which the temperature was independently registered by either a classical thermometer or thermocouples.