A neural network model of selective attention is discussed. When two patterns or more are presented simultaneously, the model successively pays selective attention to each one, segmenting it from the rest and recognizing it separately. In the presence of noise or defects, the model can recall the complete pattern in which the noise has been eliminated and the defects corrected. These operations can be successfully performed regardless of deformation of the input patterns. This is an improved version of the earlier model proposed by the author: the ability of segmentation is improved by lateral inhibition.