1. A contamination level estimation method for high voltage insulators, comprising the steps of: building an image data set of a collection of naturally polluted insulator samples; estimating equivalent salt deposit density (ESDD) levels of the collection, the ESDD estimations being used for training a Multi-layer Feed-forward Neural Network (MFNN); forming a grayscale model from an RGB representation of each of the images; a first stage segmentation step comprised of segmenting the grayscale insulator images to exclude an insulator cap from the image using matrix manipulation; a second stage edge based segmentation step comprised of segmenting the grayscale insulator images to exclude the background; transforming the RGB representation of each of the images to an HSV representation of each of the images; extracting statistical and algebraic features from the HSV representation of each of the images, wherein the HSV segmented images are used to assess the contamination level that would lead to a flashover; using the statistical and algebraic features and the ESDD estimates to train the (MFNN) to correlate levels of contamination captured in insulator images with ESDD levels, the contamination levels being light pollution level, medium pollution level, heavy pollution level and very heavy pollution level categories; classifying live insulator images using the MFNN; and outputting to a user the classified high voltage insulator pollution levels as a corollary to equivalent salt deposit density (ESDD).