In this paper, several approaches including K-means, fuzzy K-means (FKM), fuzzy adaptive resonance theory (ART2) and fuzzy Kohonen self-organizing feature mapping (SOFM) are adapted to segment the texture image. In our tests five features, energy, entropy, correlation, homogeneity, and inertia, are used in texture analysis. The K-means algorithm has the following disadvantages: (1) supervised learning mode, (2) slow real-time ability, (3) instability. The FKM algorithm has improved the performance of the instability by means of the introduction of fuzzy distribution functions. The fuzzy ART2 has advantages, such as unsupervised training, high computation rates, and a great degree of fault tolerance (stability/plasticity). Fuzzy operator and mapping functions are added in the network to improve the generality. The fuzzy SOFM integrates the FKM algorithm into fuzzy membership value as a learning rate and updates stratifies of the Kohonen network. This yields automatic adjustment of both the learning rate distribution and update neighborhood, and has an optimization problem related to FKM. Therefore, the fuzzy SOFM is independent of the sequence of feed of input patterns whereas final weight vectors by the Kohonen method depend on the sequence. The fuzzy SOFM is `self-organizing' since the `size' of the update neighborhood and learning rate are automatically adjusted during learning. Clustering errors are reduced by fuzzy SOFM as well as better convergence. The numerical results show that fuzzy ART2 and fuzzy SOFM are better than K-means algorithms. The images segmented by the algorithms are given to prove their performances.