Machine/part grouping problems have proven to be NP-complete and cannot be solved in polynomial time. Solving such problems of reasonable size often relies on heuristic approaches. Recently, several metaheuristic approaches have emerged as efficient tools for solving such problems. However, the development and implementation of such meta-heuristics have not been a trivial issue. The merits of each method and the problems involved in implementation may not be easily apprehended by practitioners, thereby posing difficulties in the selection of an efficient heuristic for industrial applications. For this reason, a comparative study of three important metaheuristic approaches—simulated annealing, genetic algorithms and tabu search for both binary (considering only machines and part types) and comprehensive (involving machine/part types, processing times, lot sizes, and machine capacities) machine grouping problems—was carried out. To test the performance of the three metaheuristics, two binary performance indices and two generalized performance indices were respectively used for binary and comprehensive machine/part grouping problems. The comparisons were made in terms of solution quality, search convergence behaviour and presearch effort. The results indicate that simulated annealing outperforms both genetic algorithm and tabu search particularly for large problems. The genetic algorithm seems slightly better than the tabu search method for the comprehensive grouping problems.