Heuristic rules are appropriate, if a decision maker wants to set the price of a new product or of a product, whose past price variation is low, and budget limitations prevent the use of marketing experiments or customer surveys. Whereas such rules are not guaranteed to provide the optimal price, generated profits should be as close as possible to their optimal values. We investigate eleven pricing rules that do not require that a decision maker knows the price response function and its parameters. We consider monopolistic market situations, in which sales depend on the price of the respective product only. A Monte Carlo simulation that is more comprehensive than extant attempts found in the literature, serves to evaluate these rules. The best performing rules either hold price changes between periods constant or make them dependent on the previous absolute price difference. These rules also outperform purely random price setting, which we use as benchmark. On the other hand, rules based on arc elasticities or on a loglinear approximation to sales and prices, turn out to be even worse than random price setting. In the conclusion, we discuss how heuristic pricing rules may be extended to deal with product line pricing, additional marketing variables (e.g., advertising, sales promotion, and sales force) and a duopolistic market situation.