This paper conveys the application of genetic algorithms（GA） which are used to improve unsupervised training and thereby increase the classification accuracy of remotely sensed data. The genetic competitive learning algorithm（GA-CL）

an integrated approach of the GA and simple competitive learning（CL） algorithm

was developed for unsupervised training. Genetic algorithms are used to improve the training results for the algorithm. GA is used to prevent falling in the local minima during the process of cluster prototypes learning. The evaluation of the algorithm uses the Jeffries-Matusita（J-M） distance

a measure of statistical separability of pairs of trained clusters. Experiments on Landsat Thema-tic Mapper（TM） data show that the GA improves the simple competitive learning algorithm. Comparisons with other unsupervised training algorithms

the K-means

GA-K-means

and the simple competitive learning algorithm are provided.