Successful exploratory analysis of process data often depends on the extraction and visualization of compact representative features of the data. This is usually accomplished via the construction of a model that relates the original higher-dimensional set of variables to a set of lower-dimensional features. In complex process systems, non-linear models such as neural networks are often the only way of extracting compact (2D or 3D) variable sets. By making use of evolutionary Computation, a population of mapping functions are constructed, which provides a more natural approach to deal with the large number of local minima in the error surfaces associated with the optimization of the mapping functions. In addition. relatively simple, explicit mapping functions can be extracted which may be more useful in applications such as the monitoring of multivariate process systems.