This document presents a method to classify skin cancer images as malignant or benign using color and texture feature extraction with support vector machine (SVM) and convolutional neural network (CNN) classifiers. The method segments skin cancer images from the ISIC dataset using active contour modeling. Color features are extracted using histogram analysis in HSV color space. Texture features like mean, variance, skewness and kurtosis are calculated statistically. Both SVM and CNN are used to classify the images based on these features, and CNN achieves higher average accuracy than SVM. The CNN approach is therefore proven more effective for skin cancer classification using color and texture features.