Signal and image processing are active areas of research in both statistics and engineering. Most of this research has emphasized the reconstruction of a "true" underlying pattern from one measured with noise. Our research has a different goal: recognition or prediction of an ancillary quantity y associated with each observed pattern x(t). We propose a nonlinear regularized regression technique, variable fusion. Variable fusion produces models of a simple parsimonious form, readily explained to the non-statistician and possibly affording savings in data collection. In addition, variable fusion models perform well in terms of prediction. In this paper we assume that the quantity y is real and single-valued and the pattern x(t) is a "signal", i.e., the space of index values t is one-dimensional, although we describe the generalization of the method to a multidimensional index space. We use the patterns as the predictors of y. The patterns generally originate as analog signals and are measured at a large set of discrete index values, giving rise to a correspondingly large set of predictor variables. The problem is therefore ill-posed and requires regularization. Variable fusion regularizes by exploiting the spatial nature of the predictor variable index through powerful variable bandwidth nonlinear smoothing analogs based on adaptive splines. We compare this method with partial least squares, ridge regression and cubic spline smoothing. The first two of these methods apply regularization that is equivariant to the labeling of the predictors. They therefore ignore the spatial nature of the predictor index. The latter method, cubic spline smoothing, is linear and cannot as readily adapt to sharp structure as can a nonlinear method. We compare the methods in Monte Carlo simulation and on two examples: phoneme classification based on log-periodograms of spoken words, and the prediction based on psychometric data of a post-traumatic stress disorder diagnostic test score.