We introduce a variant of the Perceptron algorithm called second-order Perceptron algorithm, which is able to exploit certain spectral properties of the data. We analyze the second-order Perceptron algorithm in the mistake bound model of on-line learning and prove bounds in terms of the eigenvalues of the Gram matrix created from the data. The performance of the second-order Perceptron algorithm is affected by the setting of a parameter controlling the sensitivity to the distribution of the eigenvalues of the Gram matrix. Since this information is not preliminarly available to on-line algorithms, we also design a refined version of the second-order Perceptron algorithm which adaptively sets the value of this parameter. For this second algorithm we are able to prove mistake bounds corresponding to a nearly optimal constant setting of the parameter.