The paper concerns the measurement and forecasting of technological change, a topic relevant to many high-tech organizations and their customers. We revisit recent and classic datasets from technology forecasting data envelopment analysis (TFDEA) research and technometrics in light of a new visualization technique known as t-distributed stochastic neighbor embedding (t-SNE). The technique is a nonlinear visualization technique for preserving local structure in high-dimensional spaces of data. The technique may be classified as a form of topological data analysis. Specifically, each point in the space represents a potential technological design or implementation, and each line segment in the space represents a local measure of technological improvement or degradation. We hypothesize five distinct kinds of performance development in technology within this space including the frontier, the fold, the salient, the soliton, and the lock-in. Then we examine the spaces to determine which kinds of development are the best explanations for the observed development. The technique is not extrapolative, and therefore cannot fully supplant existing technometric methods. Nonetheless, the approach offers a useful diagnostic to existing technometric methods, and may help advance theories of technological development.