‎There are two main approches to the fuzzy regression (more precisely‎: ‎regression in fuzzy environment)‎: ‎the least of sum of distances (including two methods of least squared errors and least absolute errors) and the possibilistic method (the method of least whole vaguness under some restrictions)‎. ‎Beside‎, ‎some heuristic methods have been proposed to deal with fuzzy regression‎. ‎Some of them are based on a combination of two mentioned approaches‎. ‎Some of them are based on computational algorithmes‎. ‎A few of heuristic methods use the fuzzy inference systems‎. ‎Also‎, ‎there are some methods based on clustering‎, ‎artificial neural networks‎, ‎evolutionary algorithms‎, ‎and nonparametric procedures‎.

‎In this paper‎, ‎a history and basic ideas of the two main approaches to‎ ‎fuzzy regression are reveiwed‎, ‎and some heuristic methods in this topic are investigated‎. ‎Moreover‎, ‎10 criterion are proposed by which one can‎ ‎evaluate and compare fuzzy regression models‎.