Dermatoscopy is one of the most popular non-invasive methods of skin tumors diagnostics. Digital dermatoscopy allows one to perform automatic data processing and lesions classification that significantly increases diagnostics accuracy compared to general physicians. In this article, we propose a dermatoscopy tool equipped software automatic classifier of dermatoscopic data. Noise reduction and image histogram equalization were performed during the initial step of preprocessing. After this step, a feature-detection step was performed; the program founds region of interest and calculates Haar transform, linear binary patterns, and color-texture features in different color spaces (RGB, HSV and LAB) for both tumor and healthy skin areas. Finally, evaluated features are used for classification by using Support Vector Machines (SVM). This classifier has been trained and tested using 160 dermatoscopic images made with polarized backscattered light. The article shows data for two classes separation: malignant melanoma versus non-melanoma tumors and malignant versus benign lesions. Proposed approach has achieved sensitivity of 83% and specificity of 65% for melanoma versus non-melanoma classification and sensitivity of 61% and specificity of 60% for malignant versus benign lesion classification. Performed cross-validation ensures stability of the classifier.