Motivation: Array comparative genomic hybridization (arrayCGH) is widely used to measure DNA copy numbers in cancer research. ArrayCGH data report log-ratio intensities of thousands of probes sampled along the chromosomes. Typically, the choices of the locations and the lengths of the probes vary in different experiments. This discrepancy in choosing probes poses a challenge in integrated classification or analysis across multiple arrayCGH datasets. We propose an alignment-based framework to integrate arrayCGH samples generated from different probe sets. The alignment framework seeks an optimal alignment between the probe series of one arrayCGH sample and the probe series of another sample, intended to find the maximum possible overlap of DNA copy number variations between the two measured chromosomes. An alignment kernel is introduced for integrative patient sample classification and a multiple alignment algorithm is also introduced for identifying common regions with copy number aberrations. Results: The probe alignment kernel and the MPA algorithm were experimented to integrate three bladder cancer datasets as well as artificial datasets. In the experiments, by integrating arrayCGH samples from multiple datasets, the probe alignment kernel used with support vector machines significantly improved patient sample classification accuracy over other baseline kernels. The experiments also demonstrated that the multiple probe alignment (MPA) algorithm can find common DNA aberrations that cannot be identified with the standard interpolation method. Furthermore, the MPA algorithm also identified many known bladder cancer DNA aberrations containing four known bladder cancer genes, three of which cannot be detected by interpolation.