A big problem with projecting data of a given number of dimensions to a smaller number of dimensions is that you invariably lose information regarding the spatial relationships of the data. N-D brushing is a way of recovering some of this lost information by highlighting data points which fall into a user-defined subspace. Thus, one could say ``show me the points within a (Euclidean) distance of N from a certain location.'' A brush is completely defined by its shape, size, and location. We assume a hyper-parallelepiped (a fancy name for an N-D box), and the user specifies the size of the box in each dimension. Then a location in N-D space is specified as a center point for the brush. Any data point falling within the N-D brush can have an operator applied to it, such as highlighting or masking.