The objective of this work is to develop a system that will facilitate the automated characterization of ultrasonic imaging carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 166 images were collected which were classified into: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted: first order statistics, spatial gray level dependence matrices, gray level difference statistics, neighbourhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. A modular neural network classifier was developed composed of self-organizing map (SOM) classifiers, achieving an overall diagnostic yield of 76.4%. The results of this work show that it is possible to identify a group of patients at risk of stroke based on texture features.