Biological systems have been an inspiration in the development of prototype-based clustering and vector quantization algorithms. The two dominant paradigms in biologically motivated clustering schemes are neural networks and, more recently, biological immune systems. These two biological paradigms are discussed regarding their benefits and shortcomings in the task of approximating multi-dimensional data sets. Further, simulation results are used to illustrate these properties. A class of novel hybrid models is outlined by combining the efficient use of a network topology of the neural models and the power of evolutionary computation of immune system models.