Texture is an important property of the images. Its inclusion in digital classification is known to improve the classification accuracy. In the present study, the texture features angular second moment, entropy and inverse difference moment were used to differentiate and classify forests affected by jhum (shifting cultivation) in north-eastern India. Large increases (11·1 per cent) in the classification accuracy were observed when texture and tone were used simultaneously. In general, the inverse difference moment was found to be more useful than the entropy. The angular second moment was not useful. The most accurate classification was achieved with a combination of the tone, the entropy and the inverse difference moment.