In this work, we have used multispectral imaging technology to classify cassava leaves infected by African mosaic virus by the use of their unique spectral finger print. The spectra are extracted from transmission, reflection and diffusion of their multispectral images; they have been then analyzed with statistical multivariate analysis techniques. Principal component analysis (PCA) has been used followed by K-means and Ascending Hierarchical Classification (AHC) to endorse the classification. The contribution of this work is the use of multispectral imagery which binds both spatial and spectral information to differentiate and sort infected leaves. The results show that the multimodal and imaging spectroscopy may allow blind identification and characterization of infected leaves.