Constructive learning algorithms offer an approach to incremental construction of near-minimal artificial neural networks for pattern classification. Examples of such algorithms include Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer networks of threshold logic units (or, multi-layer perceptrons). These algorithms differ in terms of the topology of the networks that they construct which in turn biases the search for a decision boundary that correctly classifies the training set. This paper presents an analysis of such algorithms from a geometrical perspective. This analysis helps in a better characterization of the search bias employed by the different algorithms in relation to the geometrical distribution of examples in the training set. Simple experiments with non linearly separable training sets support the results of mathematical analysis of such algorithms. This suggests the possibility of designing more efficient constructive algorithms that dynamically choose among different biases to build near-minimal networks for pattern classification.