High-resolution datasets from Airborne Laser Scanning (ALS) provide information to extract the outline of single tree crowns. Laser echoes with spatial coordinates inside these single-tree crowns give the ability of measuring biophysical properties and to classify species of these single-trees. Species classification by ALS-data is based on differences in crown shape, crown density, reflectivity and distribution of foliage and branches between tree species. All of these parameters may be expressed by spatial coordinates of the point cloud or by the intensity of the backscattered signal measured by ALS. In this study we investigate mean intensity and standard deviation of intensity computed for single trees by explorative data analysis and linear discriminant analysis. We explore differences in spruce, birch, and aspen trees for different echo categories from a multiple return ALS system. We found that intensity could assist species discrimination. The overall classification accuracies obtained were from 68 to 74%, depending on number of variables considered. In spite of the heterogeneous structure of the forest studied, the classification accuracy was fairly high. Intensity metrics computed from different echo categories influence overall accuracies by 3 to 4%, depending on the intensity metric used in the classification. Both species reflectivity and structural characteristics within the tree crown will influence intensity recorded by ALS.