An important measure in various stages of oil sand mining is particle size distribution (PSD) of oil sand particles. Currently PSD is found by time consuming manual inspection. An effective automation of PSD computation can play a significant role in improving the mining process. Toward this goal we propose an algorithm (snake-PCA) to detect oil sands from conveyor belt images, which pose considerable challenges to automated analysis. The novelty in snake-PCA is as follows. First, snake-PCA evolves a number of snakes based on a novel variation of gradient vector flow requiring only a point as initialization. Oil sand is then detected by applying a threshold on PCA reconstruction error of a novel pattern image formed on each evolved snake. We show the discriminative property of the proposed pattern image here. Also, our detection experiments with snake-PCA produce a PSD matching well with a manually found PSD.