Multiobjective programming (MOP) has been applied to decision making environments under conflict. Using the fuzzy set theoretic approach, this paper deals with a special type of MOP where fuzzy or imprecise objective functions are of linear fractional structure. The goal is then to optimally compromise on each objective by maximizing the aggregate membership function. The formulation for finding the best compromise solution of the model becomes a nonconvex nonlinear programming problem. As a means to alleviate the associated computational difficulty, a sequence of linear inequality problems is generated by parameterizing the objective values. We propose an iterative solution method which alternates between Khachiyan's ellipsoid method for a linear inequality problem, and an ascent method for updating the objective value. To demonstrate the efficiency of the proposed solution method as well as the practicality of our fuzzified model of multiobjective fractional programming, an example in the area of financial planning is provided.