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A separation algorithm applicable to the pattern classification and cluster analysis of n-dimensional (n > 2) data is presented. The algorithm reduces the dimensionality of the problem by projecting each point into a plane. This plane is presented to the user on a computer graphics console screen. The operator picks a point on the screen with a lightpen and chooses a \"direction of movement\" to achieve or increase separation, thereby causing an iteration of the algorithm. Each iteration is in fact a reorientation of the plane into which the data points are projected. Iterations continue until satisfactory separation is achieved. The algorithm is not restricted by the dimensionality of the data, nor are any distributional assumptions required. Results from six case studies indicate that the algorithm is a useful tool for the analysis of multidimensional data.