Feature contribution means that what features actually participates more in grouping data patterns that maximizes the system’s ability to classify object instances. In this paper, modified K-means fast learning artificial neural network (K-FLANN) was used to cluster multidimensional data. The operation of neural network depends on two parameters namely tolerance (δ) and vigilance (ρ). By setting the vigilance parameter, it is possible to extract significant attributes from an array of input attributes and thus determine the principal features that contribute to the particular output. Exhaustive search and Heuristic search techniques are applied to determine the features that contribute to cluster data. Experiments are conducted to predict the network's ability to extract important factors in the presented test data and comparisons are made between two search methods.