Developmental trajectories are defined as curves of repeated observations. Individuals may differ in the starting point, the degree of acceleration or deceleration, the timing of acceleration or deceleration, overall shape, elevation, and scatter of curves. This article discusses methods for typifying developmental trajectories. Two groups of methods are considered. The first group involves assigning individuals to a priori existing trajectories and counting the number of individuals that reflect natural groupings of trajectories based on categorical classifications, using Configural Frequency Analysis (CFA). The second method involves employing methods of cluster analysis. When selecting a method of cluster analysis, the following ten cluster characteristics need to be considered: (1) disjoint vs. overlapping clusters; (2) hierarchical vs. non-hierarchical clustering; (3) agglomerative vs. divisive clustering; (4) exhaustive vs. selective classification; (5) stochastic vs. deterministic clustering; (6) clustering based on correlation vs. distance measure; (7) convex vs. non convex clusters; (8) clustering based on symmetric vs. asymmetric measure; (9) monothetic vs. polythetic classification, and (10) manifest versus latent variable clustering. A review of clustering methods is presented using examples to demonstrate the pros and cons of each method. Discriminant analysis and logistic regression are discussed as methods for subsequent analysis of groupings. Examples are presented using artificial data and empirical data on the development of cigarette smoking in male adolescents.