The objective of thinning is to reduce the amount of information in image patterns to the minimum needed for recognition. Thinned image helps the extraction of important features such as end points, junction points, and connections from image patterns. The ultimate goal of parallel algorithms is to minimize the execution time while producing high quality thinned image. Though much research has been performed for parallel thinning algorithms, there has been no systematical approach for comparing the execution speed of parallel thinning algorithms. Several rough comparisons have been done in terms of iteration numbers. But, such comparisons may lead to wrong guides since the time required for iterations varies from one algorithm to the other algorithm. This paper proposes a formal method to analyze the performance of parallel thinning algorithms based on PRAM (Parallel Random Access Machine) model. Besides, the quality of skeletons, robustness to boundary noise sensitivity, and execution speed are considered. Six parallel algorithms, which shows relatively high performance, are selected, and analyzed based on the proposed analysis method. Experiments show that the proposed analysis method is sufficiently accurate to evaluate the performance of parallel thinning algorithms.