This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems.The difficulties of such classification include:1) the shape of the same algae category is deformable,and largely variant due to the individual differences and mature stage;2) the image of algae may vary due to different 3D positions to the imaging plane and partial occlusion;3) the images also contain unknown algae and contaminations.In the proposed method,several shape features were developed,a naive Bayes classifier (NBC) was trained to reject the contaminative objects and unknown algae,and a support vector machine (SVM) was used to classify the algae to taxonomic categories.Our approach achieved greater 90% accuracy on a collection of algal images.The test on contaminated algal image set (containing unknown algae and non-algae objects such as sands) also demonstrated promising results.