Several industrial coal processes are largely determined by the distribution of particle sizes in their feed.Currently these parameters are measured by manual sampling,which is time consuming and cannot provide real time feedback for automatic control purposes.In this paper,an approach using image segmentation on images of overlapped coal particles is described.The estimation of the particle size distribution by number is also described.The particle overlap problem was solved using image enhancement algorithms that converted those image parts representing material in lower layers to black.Exponential high-pass filter(EHPF) algorithms were used to remove the texture from particles on the surface.Finally,the edges of the surface particles were identified by morphological edge detection.These algorithms are described in detail as is the method of extracting the coal particle size.Tests indicate that using more coal images gives a higher accuracy estimate.The positive absolute error of 50 random tests was consistently less than 2.5% and the errors were reduced as the size of the fraction increased. Several industrial coal processes are largely determined by the distribution of particle sizes in their feed. Currently these parameters are measured by manual sampling, which is time consuming and can not provide real time feedback for automatic control purposes. In this paper, an approach using image segmentation on images of overlapped coal particles is described. The estimation of the particle size distribution by number is also described. particle overlap problem was solved using image enhancement algorithms that converted those image parts representing material in lower layers to black. Exponential high-pass filter (EHPF) algorithms were used to remove the texture from particles on the surface .Finally, the edges of the surface particles were identified by morphological edge detection.These algorithms are described in detail as is the method of extracting the coal particle size.Tests indicate that using more coal images gives a higher accuracy estimate. The positive absolute error of 50 random tests was consistently less than 2.5% and the errors were reduced as the size of the fraction increased.