In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network; (2) a multiple linear regression; (3) the forest canopy density mapper; and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. The field study was conducted in February-March 2003 and September-October 2003 in a forest corridor linking the Himalayan middle mountains to the Royal Chitwan national park at Chitwan district, Nepal. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z -test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of rankedcategories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.