Data complexity analysis aims at providing a scientific basis to relate behavior of classifiers to certain intrinsic characteristics in the data available for training the classifiers. The analysis seeks explanations of a classifier's observed performance variability in different tasks, and thereby provides some guidance on selecting a classifier method for a given task. The data complexity measures can also be used to evaluate alternative set-ups of a classification tasks, or different feature transformations on how they may impact the difficulty of the underlying task. In a way, the complexity measures are ``features'' of a classification task, which can support meta-learning for the decisions to be made for each task, e.g. which classifier to use.