Hyperdimensional computing (HDC) refers to the representation and manipulation of data in a very high dimensional space using random vectors. Due to the high dimensionality, vectors of the space can code large amounts of information in a distributed manner, are robust to variation, and are easily distinguished from random noise. More importantly, HDC can be used to represent compositional and hierarchical relationships and recursive operations between entities using fixed-size representations, making it intriguing from a cognitive modeling point of view. However, the majority of the existing work in this area has focused on modeling discrete categorical data. This paper presents a new method for mapping continuous-valued multivariate data into hypervectors, enabling construction of compositional representations from non-categorical data. The mapping is studied in a word classification task, showing how rich distributed representations of spoken words can be encoded using HDC-based representations.