Computational synthesis tools that automatically generate solutions to design problems are not widely used in architectural practice despite many years of research. This deficiency can be attributed, in part, to the difficulty of constructing robust building specific databases. New advances in artificial intelligence such as Hierarchical Temporal Memory (HTM) have the potential to make the construction of these databases more realistic in the near future. Based on an emerging theory of human neurological function, HTMs excel at ambiguous pattern recognition. This paper includes a first experiment using HTMs for learning and recognizing patterns in the form of visual style characteristics in three distinct chair back types. Results from the experiment indicate that HTMs develop a similar storage of quality to humans and are therefore a promising option for capturing multi-modal information in future design automation efforts.