A method of deconvoluting 2-dimensional particle size distributions from chord size data is presented and evaluated. This is the Probability Apportioning Method (PAM3). It assumes that the particles (or droplets) can be represented by super quadrics and are cut randomly by a sensor to give a chord measurement. Starting from an assumed uniform particle distribution, Bayes' theorem is used to calculate hit probabilities for each particle type and the population is then recalculated. The process is then repeated until there is no significant further change in the calculated distribution. Using numerical simulations PAM3 is shown to be quite accurate and robust for a number of different types of particle shapes provided there is a sufficient number of accurate measurements.