Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.