Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. In our analysis, we use images depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. We model morphology with a novel formulation of Conditional Random Fields, a latent state CRF, to capture the highly varying compartment-based structure of the neurons (soma-axon-dendrites). We integrate a multi-class logistic model as the local potential function for combining compartment features. All parameters are learned in a single procedure, while L1-norm logistic model parameters are added in the maximum pseudo-likelihood model for learning with better scalability. The regularization hyper-parameters are chosen with a minimum cross-validation generalization error model.