We introduce a novel multivariate approach for solving weighted parameterized problems. In our model, given an instance of size $n$ of a minimization (maximization) problem, and a parameter $W \geq 1$, we seek a solution of weight at most (or at least) $W$. We use our general framework to obtain efficient algorithms for such fundamental graph problems as Vertex Cover, 3-Hitting Set, Edge Dominating Set and Max Internal Out-Branching. The best known algorithms for these problems admit running times of the form $c^W n^{O(1)}$, for some constant $c>1$. We improve these running times to $c^s n^{O(1)}$, where $s\leq W$ is the minimum size of a solution of weight at most (at least) $W$. If no such solution exists, $s=\min\{W,m\}$, where $m$ is the maximum size of a solution. Clearly, $s$ can be substantially smaller than $W$. In particular, the running times of our algorithms are (almost) the same as the best known $O^*$ running times for the unweighted variants. Thus, we solve the weighted versions of * Vertex Cover in $1.381^s n^{O(1)}$ time and $n^{O(1)}$ space. * 3-Hitting Set in $2.168^s n^{O(1)}$ time and $n^{O(1)}$ space. * Edge Dominating Set in $2.315^s n^{O(1)}$ time and $n^{O(1)}$ space. * Max Internal Out-Branching in $6.855^s n^{O(1)}$ time and space. We further show that Weighted Vertex Cover and Weighted Edge Dominating Set admit fast algorithms whose running times are of the form $c^t n^{O(1)}$, where $t \leq s$ is the minimum size of a solution.