Fluorescence microscopy is rapidly becoming a multi- dimensional technique. Many applications generate similar data analysis problems. Whatever the non-spatial dimension (time, energy), users have to make the choice between local analysis and global analysis. For local analysis, the evolution of pixels (or regions of interest) is modeled as a function of the external parameter. Results are displayed as parametric images. For global analysis, multivariate statistical analysis can be used to extract and interpret the significant information (in the presence of redundancy and noise) in the form of eigenimages and eigenfactors. Automatic classification methods start to play a role for the co-location problem, in which pixels are classified into regions corresponding to positive, null or negative correlation. With two or three images, the scatterplot (an estimation of the joint probability density function), can be built. Interactive and automatic correlation partitioning (ICP, ACP) can then be performed. The method we have developed (Parzen estimate of the probability density function followed by the watersheds mathematical morphology approach) does not make assumptions about the shape of clusters. With more than three images, dimensionality reduction must be applied, for visualization purposes and for simplifying classification. This can be done by linear or non-linear methods such as Multi-Dimensional Scaling, Auto-Associative Neural Networks or Self-Organizing Mapping.