Abstract: A new adaptive weighted immune clonal instance selection algorithm (wICISA) for underwater acoustic targets classification is proposed in this paper. The wICISA gets the weight of instances through the Adaboosting algorithm, in each iteration, the affinity of antibodies and the number of clones of antibodies are calculated with the weight of instance. And then, new generations are generated through repetitive application of clonal copy, antibody update (Hamming distance crossover and Weight Reduced Nearest Neighbor mutation) and clonal selection operation. The time wave structure features, wavelet analysis features and auditory spectrum features are extracted from 3 classes of underwater targets, and are used in instance selection and classification experiments. Experimental results show that wICISA can select the subsets of efficient instances with about 2% increase in the accuracy of SVM classifier when the number of instances decreases by about 82%. wICISA has good convergence and stability, and the instance subset obtained by wICISA achieves with good generalizability, and it can reduce the classification time remarkably.