Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable 'long call' vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We also used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrograms. We found low inter-observer reliability and poor classification accuracy using supervised approaches, indicating that pulse types were not discrete. We propose a new pulse type classification scheme that is highly reproducible across observers and exhibits high classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and measure the gradedness of animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-á-vis graded repertoires.