The early analysis of pigmented skin lesions is important for clinicians in order to recognize malignant melanoma. However, it is difficult to differentiate it from benign skin lesions due to their similarity based on their appearance. Since melanoma has a tendency to grow inside the skin and the depth of penetration of light into the skin is wavelength dependent, a multispectral imaging acquisition and processing approach to classify pigmented lesions as melanoma seems appropriate. This paper presents a method to diagnose melanoma lesions over a group of 26 samples acquired with a multispectral system, where 6 of them are melanomas, and the other 20 are other types of pigmented lesions. A Leave-One-Out strategy is used to create the training/test set. The classification imbalance problem inherent to this dataset is alleviated using a SMOT E technique. The random component of the SMOT E methodology is dealt with running it 25 times and a Qualified Majority Voting (QMV ) scheme is used to do the final classification, using SV M. Results show this strategy allows to obtain competitive classification quality results.