It is often reported in the technique literature that the success of the self-organizing feature map (SOM) formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood set. In this paper, we propose a healing mechanism to repair feature maps that are not well topologically ordered. The healed map is then further fine-tuning by the SOM algorithm to improve the accuracy of the map. Two data sets are tested to illustrate the performance of the proposed method.