Constructive learning algorithms offer an approach for dynamically constructing near-minimal neural network architectures for pattern classification tasks. Several such algorithms proposed in the literature are shown to converge to zero classification errors on finite non-contradictory datasets. However, these algorithms are restricted to two-category pattern classification and (in most cases) they require the input patterns to have binary (or bipolar) valued attributes only. We present a provably correct extension of the upstart algorithm to handle multiple output classes and real-valued pattern attributes. Results of experiments with several artificial and real-world datasets demonstrate the feasibility of this approach in practical pattern classification tasks, and also suggest several interesting directions for future research.