The detection of chromosomal aberrations constitutes an important stage in the comprehension of the pathogenesis of several diseases, as cancer. In this case, the amplification or deletion (suppression) of some chromosomal regions, was identified as being an essential mechanism of tumour development. Several methods were proposed for the detection of regions, which copy number is altered, from aCGH data. However, it has been reported that 40-60 % of genes show changes in RNA abundance, when amplified [27, 38]. For this reason, the detection of chromosomal aberration from RNA abundance seems solvable. To achieve that, we propose, initially, a simple method, based on cumulated sums (CUMSUM), then more elaborated method, based on the hidden Markov models "HMM" . Within the framework of the HMM, we propose a model HMM with two states (HMM2s) for the detection of only one type of aberration (amplification or deletion), a model HMM with three states (HMM3s) for the simultaneous detection of the amplifications and deletions. We also propose a model HMM with 2 states and double observations by state (HMM2s2o) for the simultaneous analysis of the expression and aCGH data. We tested the various models on simulated and real data, and finally we compared the detected regions of aberration from expression data to those detected from aCGH data of the Glioblastoma.