This work presents an application of an improved version of the WiSARD weightless neural network in the recognition of different mutation types of HIV-1 and in the determination of antiretroviral drugs resistence. The data set used consists of 1205 gene sequence of the HIV-1 protease of subtypes B, C and F from patients under treatment failure. Experiments performed with the bleaching technique over the WiSARD model under different data representation strategies have shown promising results, both in terms of accuracy and standard deviation.