Abstract This paper describes a new method for multilevel threshold selection of gray level images. The proposed method includes three main stages. First, a hill-clustering technique is applied to the image histogram in order to approximately determine the peak locations of the histogram. Then, the histogram segments between the peaks are approximated by rational functions using a linear minimax approximation algorithm. Finally, the application of the one-dimensional Golden search minimization algorithm gives the global minimum of each rational function, which corresponds to a multilevel threshold value. Experimental results for histograms with two or more peaks are presented.