Cloud detection is a crucial step in climate modeling and prediction. The Multi-angle Imaging SpectroRadiometer (MISR) was launched in 1999 by NASA in part to provide new and better methods for detecting clouds and estimating their heights. MISR looks at Earth and its atmosphere at view angles and four spectral bands thus providing a hyperstereo capability. Even so, cloud detection remains difficult in scenes covered with ice and snow. In this paper, we discuss a new methodology that bypasses the cloud height estimation step to directly tackle cloud detection using features of ice/snow (no cloud) pixels from obtained from different MISR view angles. We propose the linear correlation matching classification (LCMC) algorithm, which is based on Fisher linear correlation tests. We compare LCMC with the MISR Level 2 top-of-the atmosphere cloud algorithm (known as "L2TC"), and find that LCMC gives better coverage and more robust results as judged by visual inspection of finer resolution images. LCMC can also detect the very thin clouds in many cases. Moreover, LCMC is computationally much faster than L2TC and easier to implement. We hope to combine LCMC with L2TC in the future to improve the accuracy of the L2TC cloud height retrieval.