||There have been many efficiency frontier analysis methods reported in the literature. However, each of these methodologies has its strength as well as major limitations. This study proposes a Meta heuristic approach based on adaptive neural network (ANN) technique, fuzzy C-means and numerical taxonomy (NT) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Homogenous test is done by NT. It is used to determine if the DMUs are homogenous or not. The proposed computational methods are able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to za has been used. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in non homogenous situation, for increasing DMUs’ homogeneousness, fuzzy C-means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. Homogenous test result is positive in the first example, which deals with power generation sectors, and is negative in the second example dealing auto industries of various developed countries. Overall, we find that the proposed integrated algorithm based on ANN, fuzzy C-means and numerical taxonomy provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.